{
  "single-agent": {
    "summary": {
      "method": "single-agent",
      "dataset": "MultiGov-30-v3-claude-sonnet-4-6",
      "n_questions": 30,
      "avg_em": 0.0,
      "avg_f1": 0.6590429482632548,
      "avg_latency_ms": 6241.623596666977,
      "avg_cost_usd": 0.06620739999999999,
      "avg_tokens": 326781.1,
      "avg_rounds": 1.0,
      "avg_nodes": 1.0,
      "total_cost_usd": 1.9862219999999995,
      "total_latency_s": 187.24870790000932,
      "total_shacl_pass": 0,
      "total_shacl_fail": 0,
      "total_constraint_propagations": 0,
      "total_observer_redirects": 0,
      "total_ontology_route_filtered": 0,
      "avg_governance_accuracy": 0.0,
      "avg_framework_fidelity": 0.0,
      "avg_scope_adherence": 0.0,
      "avg_cross_reference_score": 0.0,
      "avg_constrained_f1": 0.3295214741316274
    },
    "questions": [
      {
        "question_id": "MG-A01",
        "question": "What AI practices are completely prohibited under the EU AI Act, and what are the penalties?",
        "gold_answer": "Article 5 prohibits: subliminal manipulation, exploitation of vulnerabilities, social scoring by public authorities, real-time remote biometric identification in public spaces (with limited exceptions for law enforcement), untargeted scraping for facial recognition databases, emotion recognition in workplace and education, and biometric categorisation using sensitive characteristics. Under Article 99, violations carry fines up to EUR 35 million or 7% of worldwide annual turnover.",
        "predicted_answer": "## Prohibited AI Practices and Penalties Under the EU AI Act\n\n### Prohibited Practices (Article 5)\n\nThe following AI practices are **completely prohibited**:\n\n1. **Subliminal or manipulative techniques** that materially distort behaviour and cause significant harm\n2. **Exploiting vulnerabilities** of specific groups based on age, disability, or social/economic situation\n3. **Social scoring by public authorities** or on their behalf\n4. **Real-time remote biometric identification** in public spaces for law enforcement (with narrow exceptions for abduction victims, imminent threats to life, or genuine terrorist threats)\n5. **Untargeted scraping of facial images** from internet or CCTV for facial recognition databases\n6. **Emotion recognition** in workplaces and educational institutions\n7. **Biometric categorisation** using sensitive characteristics\n\n### Penalties\n\nViolations carry the **highest penalty tier** under the AI Act:\n- **Up to EUR 35 million or 7% of worldwide annual turnover**, whichever is **higher**\n\n### Key Enforcement Details\n\n- Enforcement began **2 February 2025**\n- Systems",
        "exact_match": 0.0,
        "f1": 1.0,
        "latency_ms": 6418.802000000142,
        "cost_usd": 0.066348,
        "total_tokens": 21113,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
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        "cross_reference_score": 0.0,
        "constrained_f1": 0.5
      },
      {
        "question_id": "MG-A02",
        "question": "What is the complete compliance chain a provider must follow to place a high-risk AI system on the EU market?",
        "gold_answer": "The provider must follow this compliance chain: (1) Implement risk management system (Art. 9); (2) Ensure data governance for training data (Art. 10); (3) Draw up technical documentation (Art. 11); (4) Ensure record-keeping and logging (Art. 12); (5) Provide transparency and instructions for use (Art. 13); (6) Design for human oversight (Art. 14); (7) Achieve accuracy, robustness and cybersecurity (Art. 15); (8) Establish quality management system (Art. 17); (9) Complete conformity assessment (Art. 43); (10) Affix CE marking (Art. 48); (11) Register in EU database (Art. 49). Only after all steps can the system be lawfully placed on the market.",
        "predicted_answer": "Based on the provided context, the complete compliance chain for placing a high-risk AI system on the EU market is:\n\n1. **Classify** the AI system per Article 6 (check Annex I and Annex III categories)\n2. **Establish** a Risk Management System (Art. 9)\n3. **Implement** Data Governance (Art. 10)\n4. **Prepare** Technical Documentation (Art. 11)\n5. **Ensure** Record-keeping capability (Art. 12)\n6. **Provide** Transparency/Instructions for Use (Art. 13)\n7. **Design in** Human Oversight measures (Art. 14)\n8. **Achieve** appropriate Accuracy, Robustness, and Cybersecurity (Art. 15)\n9. **Establish** a Quality Management System (Art. 17)\n10. **Undergo** Conformity Assessment (Art. 43) \u2014 third-party or self-assessment depending on category\n11. **Draw up** EU Declaration of Conformity (Art. 47)\n12. **Affix** CE Marking (",
        "exact_match": 0.0,
        "f1": 0.8,
        "latency_ms": 4686.425899999449,
        "cost_usd": 0.066363,
        "total_tokens": 42210,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
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      },
      {
        "question_id": "MG-A03",
        "question": "What are the obligations of deployers of high-risk AI systems regarding human oversight?",
        "gold_answer": "Under Article 26, deployers must assign human oversight to natural persons with necessary competence, training and authority. Under Article 14, the system must be designed to enable individuals to: fully understand system capacities and limitations, monitor operation, remain aware of automation bias, correctly interpret outputs, decide not to use the system, override or reverse outputs, and intervene through a stop button. For biometric identification (Annex III point 1(a)), at least two natural persons must verify and confirm any action.",
        "predicted_answer": "Based on Article 14 and Article 26 of the AI Act, deployers of high-risk AI systems have the following human oversight obligations:\n\n1. **Assign competent persons**: Deployers must assign human oversight to **competent natural persons** capable of effectively overseeing the AI system (Art. 26(b)).\n\n2. **Implement oversight measures**: Deployers must implement oversight measures identified by the provider as appropriate for the deployer to carry out, including:\n   - Tools to interpret outputs\n   - Ability to decide not to use or override the system\n   - Ability to intervene and halt the system\n\n3. **Guard against automation bias**: Oversight measures must specifically prevent over-reliance on AI outputs, particularly for decisions affecting natural persons.\n\n4. **Monitor operation**: Deployers must monitor the system's operation based on the provider's instructions for use (Art. 26(d)).\n\n5. **Ensure appropriate input data**: Deployers must ensure input data is relevant and sufficiently representative (Art. 26(c)).\n\n6. **Follow instructions for use**: Deployers must take appropriate technical and organisational measures to use the system in accordance",
        "exact_match": 0.0,
        "f1": 0.42857142857142855,
        "latency_ms": 5405.364800000825,
        "cost_usd": 0.06634499999999999,
        "total_tokens": 63301,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-A04",
        "question": "What specific requirements does the EU AI Act impose on general-purpose AI models with systemic risk?",
        "gold_answer": "Under Article 51, a GPAI model is classified as having systemic risk when training computation exceeds 10^25 FLOPs. Article 55 requires such providers to: perform model evaluations with standardised protocols including adversarial testing; assess and mitigate systemic risks at Union level; keep track of and report serious incidents to the AI Office; ensure adequate cybersecurity protection. Article 52 requires all GPAI providers to maintain technical documentation, provide information to downstream providers, and have a copyright policy.",
        "predicted_answer": "Based on the provided context, Article 51 and 55 impose the following specific requirements on GPAI models with systemic risk:\n\n1. **Model evaluation** using state-of-the-art protocols, including red teaming\n2. **Adversarial testing** to assess and mitigate systemic risks, including risks of misuse for CBRN threats, cyber-attacks, and large-scale disinformation\n3. **Incident tracking and reporting** of serious incidents and possible corrective measures to the AI Office and national authorities\n4. **Cybersecurity protections** \u2014 ensure adequate cybersecurity measures\n5. **Energy consumption documentation and reporting**\n\nA GPAI model is presumed to have systemic risk when its training computation exceeds **10\u00b2\u2075 FLOPs**.\n\nCompliance can be demonstrated through **codes of practice** (Art. 56), coordinated by the EU AI Office, which create a presumption of compliance if followed. Additionally, GPAI providers with systemic risk must make sufficient technical documentation available to downstream providers integrating their models into high-risk AI systems.\n\nThese obligations became enforceable from **2 August",
        "exact_match": 0.0,
        "f1": 0.8333333333333334,
        "latency_ms": 6618.350000000646,
        "cost_usd": 0.06635400000000002,
        "total_tokens": 84395,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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        "constrained_f1": 0.4166666666666667
      },
      {
        "question_id": "MG-A05",
        "question": "What are the GDPR principles for processing personal data and how do they apply to AI training?",
        "gold_answer": "GDPR Article 5 requires personal data to be processed with: lawfulness, fairness and transparency; purpose limitation (collected for specified purposes only); data minimisation (adequate, relevant, limited to necessary); accuracy (kept up to date); storage limitation (not kept longer than necessary); integrity and confidentiality (appropriate security). For AI training data under Article 10 of the AI Act, these principles mean training data must comply with data minimisation and purpose limitation when processing personal data.",
        "predicted_answer": "## GDPR Principles for Personal Data Processing and AI Training\n\n### The Six Core Principles (Article 5 GDPR)\n\nPersonal data must be processed in accordance with:\n\n1. **Lawfulness, fairness, and transparency**\n2. **Purpose limitation** \u2014 collected for specified, explicit, legitimate purposes; not further processed incompatibly\n3. **Data minimisation** \u2014 adequate, relevant, and limited to what is necessary\n4. **Accuracy** \u2014 kept accurate and up to date\n5. **Storage limitation** \u2014 kept no longer than necessary\n6. **Integrity and confidentiality** \u2014 appropriate security measures\n\nThe controller must also demonstrate compliance (**accountability**).\n\n---\n\n### Application to AI Training\n\nEach principle creates specific tensions and obligations:\n\n**Purpose Limitation:** Training data collected for one purpose may require a compatible purpose assessment under Art. 6(4) before being repurposed for AI training.\n\n**Data Minimisation:** This creates a fundamental tension \u2014 AI models generally perform better with more data, but GDPR requires only what is necessary. Practical resolutions include:\n- Synthetic data augmentation\n- Federated learning\n- Differential",
        "exact_match": 0.0,
        "f1": 1.0,
        "latency_ms": 5562.776700000541,
        "cost_usd": 0.06635400000000002,
        "total_tokens": 105489,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-A06",
        "question": "What transparency obligations exist for AI systems that generate deepfakes or synthetic content?",
        "gold_answer": "Article 50 requires providers of AI systems generating synthetic audio, image, video or text content to ensure outputs are marked in a machine-readable format as artificially generated or manipulated. Deployers of AI systems generating deep fakes must disclose that content has been artificially generated or manipulated. For AI systems interacting directly with persons, providers must design them so persons are informed they are interacting with an AI, unless obvious from context. Exception: AI used to detect or prevent criminal offences.",
        "predicted_answer": "Based on Article 50 of the AI Act:\n\n**Providers** of AI systems generating synthetic audio, image, video, or text must ensure outputs are **marked in a machine-readable format** as artificially generated or manipulated (Art. 50(2)).\n\n**Deployers** that generate or manipulate image, audio, or video constituting a deepfake must **disclose that the content has been artificially generated** (Art. 50(4)).\n\n**Exemptions** to deepfake disclosure include: content used for law enforcement/national security, manifestly artistic or satirical work (parody, caricature), and content constituting part of a media freedom exercise with editorial safeguards \u2014 though these exemptions are narrow.\n\nTechnical implementation approaches include C2PA metadata standards, invisible watermarking (e.g., SynthID), and statistical text watermarking in LLM outputs.\n\nThese obligations apply from **2 August 2025**, with non-compliance fines of up to **EUR 7.5 million or 1% of worldwide annual turnover** under Art. 99(5).",
        "exact_match": 0.0,
        "f1": 1.0,
        "latency_ms": 6917.025700000522,
        "cost_usd": 0.066222,
        "total_tokens": 126571,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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        "governance_accuracy": 0.0,
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      },
      {
        "question_id": "MG-A07",
        "question": "How does DORA define ICT risk management requirements for financial entities?",
        "gold_answer": "DORA Articles 5-16 require financial entities to have an internal governance and control framework ensuring effective and prudent management of ICT risk. The framework must include strategies, policies, procedures, ICT protocols and tools to protect all information assets and ICT assets including software, hardware and servers. Financial entities must identify, classify and adequately document all ICT-supported business functions.",
        "predicted_answer": "Based on the provided context, DORA requires financial entities to have an **internal governance and control framework** for effective and prudent management of all ICT risks. Specifically:\n\n- The **management body** must define, approve, oversee, and be responsible for implementing all ICT risk management arrangements\n- Entities must deploy **strategies, policies, procedures, ICT protocols and tools** to protect all information and ICT assets\n- Systems must be **updated and maintained** appropriate to the nature, variety, complexity, and magnitude of operations\n\nFor AI systems specifically, this means:\n1. Classifying all AI systems as **ICT assets**\n2. Mapping business process dependencies on AI systems\n3. Assessing risks from AI system failures or incorrect outputs\n4. Maintaining **manual fallback procedures** for AI failures\n5. Subjecting model updates and retraining to **change management procedures**\n6. Conducting **digital operational resilience testing**, including threat-led penetration testing for significant entities\n\nDORA applies to virtually all regulated financial entities (credit institutions, investment firms, insurers, etc.) and entered into application on **17 January 2025**. Importantly,",
        "exact_match": 0.0,
        "f1": 1.0,
        "latency_ms": 7074.381400001585,
        "cost_usd": 0.06633899999999998,
        "total_tokens": 147660,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
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        "observer_redirects": 0,
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        "cross_reference_score": 0.0,
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      },
      {
        "question_id": "MG-A08",
        "question": "What incident notification timelines does NIS2 require for essential entities?",
        "gold_answer": "NIS2 Article 23 requires essential entities to notify CSIRT or competent authority: (a) within 24 hours \u2014 early warning indicating whether incident is suspected of being caused by unlawful or malicious acts; (b) within 72 hours \u2014 incident notification with assessment of severity and impact; (c) upon request \u2014 intermediate report; (d) within one month \u2014 final report with detailed description, root cause, and mitigation measures.",
        "predicted_answer": "Based on the provided context, NIS2 requires essential entities to follow a **three-stage notification process**:\n\n1. **Early warning within 24 hours** of becoming aware of the incident (must indicate if unlawful/malicious acts are suspected)\n2. **Incident notification within 72 hours** \u2014 updating the early warning with an initial assessment of severity and impact\n3. **Final report within one month** after the incident notification \u2014 including detailed description, root cause analysis, mitigation measures, and cross-border impact\n\nNotifications are submitted to the entity's **CSIRT or competent authority**, which must respond within 24 hours of receiving the early warning.",
        "exact_match": 0.0,
        "f1": 1.0,
        "latency_ms": 5319.343899998785,
        "cost_usd": 0.06473400000000001,
        "total_tokens": 168642,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0,
        "governance_accuracy": 0.0,
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      },
      {
        "question_id": "MG-A09",
        "question": "What high-risk AI use cases does Annex III define for employment and workers management?",
        "gold_answer": "Annex III point 4 classifies as high-risk: AI systems for recruitment or selection of natural persons, particularly for placing targeted job advertisements, screening or filtering applications, evaluating candidates in interviews or tests. Also: AI for making decisions affecting terms of work-related relationships, promotion or termination, task allocation based on individual behaviour or personal traits, and monitoring and evaluating performance and behaviour of persons in work relationships.",
        "predicted_answer": "Based on the provided context, Annex III Category 4 covers **employment, workers management and access to self-employment**, specifically including **recruitment, performance evaluation**.",
        "exact_match": 0.0,
        "f1": 0.1839080459770115,
        "latency_ms": 2390.190899997833,
        "cost_usd": 0.06308099999999994,
        "total_tokens": 189517,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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        "governance_accuracy": 0.0,
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        "constrained_f1": 0.09195402298850575
      },
      {
        "question_id": "MG-A10",
        "question": "What is the role and scope of AI regulatory sandboxes under the EU AI Act?",
        "gold_answer": "Articles 57-58 require Member States to ensure at least one AI regulatory sandbox is operational at national level by August 2026. Sandboxes provide a controlled environment for development, testing and validation of innovative AI systems under strict oversight. They include real-world testing conditions. Personal data may be processed in sandboxes for developing AI in the public interest, with specific safeguards. SMEs and startups have priority access. Sandbox results are considered by market surveillance authorities.",
        "predicted_answer": "Based on the provided context, AI regulatory sandboxes under the EU AI Act (Articles 57-60) serve the following role and scope:\n\n**Role:** Sandboxes provide a **controlled environment** for developing, testing, and validating innovative AI systems for a limited time before market placement, operating under direct supervision and guidance of competent authorities.\n\n**Key scope elements:**\n- Member States must establish **at least one national sandbox by 2 August 2026**\n- Participants may test high-risk AI systems in real-world conditions under specific safeguards: informed consent of subjects, ability to halt testing at any time, and ongoing monitoring/oversight\n- **Prohibited AI practices (Article 5) remain prohibited** even within sandboxes \u2014 they cannot be used to test banned systems\n- Real-world testing (Article 60) requires an approved testing plan, informed consent, necessity limits, and immediate halt capability if safety concerns arise\n\n**Benefits include:**\n- Direct regulatory guidance during development\n- Legal certainty on classification and compliance\n- Streamlined conformity assessment for sandbox-developed systems\n- Access to high-quality data under controlled conditions\n\n**SME/startup provisions:** Priority access",
        "exact_match": 0.0,
        "f1": 0.8333333333333334,
        "latency_ms": 7089.514200000849,
        "cost_usd": 0.06634799999999996,
        "total_tokens": 210609,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
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        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-B01",
        "question": "How do the EU AI Act and GDPR jointly regulate automated decision-making that affects natural persons?",
        "gold_answer": "GDPR Article 22 gives data subjects the right not to be subject to solely automated decisions with legal effects, with rights to human intervention, express their view, and contest decisions. The AI Act complements this: Article 26(7) requires deployers of high-risk AI systems making decisions about natural persons to inform them of the AI system's use. Article 13 requires sufficient transparency for deployers to interpret outputs. Article 14 mandates human oversight with override capability. Together, these create a layered protection: GDPR provides the right to contest, AI Act ensures transparency and human control over the decision process.",
        "predicted_answer": "## Joint Regulation of Automated Decision-Making Under EU AI Act and GDPR\n\n### The Dual Framework\n\n**GDPR Article 22** grants individuals the right not to be subject to decisions based **solely on automated processing** that produce legal or similarly significant effects. Exceptions apply for contract necessity, legal authorization, or explicit consent \u2014 but even then, the individual retains rights to human intervention, to express their point of view, and to contest the decision.\n\n**AI Act Article 14** requires that high-risk AI systems be designed to enable **effective human oversight**, specifically guarding against automation bias \u2014 the tendency to over-rely on AI outputs without critical evaluation.\n\n---\n\n### How They Interact\n\nThe two frameworks create a **double mandate** for meaningful human involvement:\n\n- GDPR Art. 22 requires that humans have **genuine authority and discretion** to override AI decisions \u2014 not merely rubber-stamp them\n- AI Act Art. 14 requires that systems be **technically designed** to support that oversight, including tools to interpret outputs, ability to override, and interfaces that discourage automation bias\n\nTogether, they mean the human reviewer must have **actual competence and real discretion**, not",
        "exact_match": 0.0,
        "f1": 0.7142857142857143,
        "latency_ms": 7051.6038999994635,
        "cost_usd": 0.066357,
        "total_tokens": 231704,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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        "governance_accuracy": 0.0,
        "framework_fidelity": 0.0,
        "scope_adherence": 0.0,
        "cross_reference_score": 0.0,
        "constrained_f1": 0.35714285714285715
      },
      {
        "question_id": "MG-B02",
        "question": "What are the combined penalty exposures when a high-risk AI system violates both the EU AI Act and GDPR?",
        "gold_answer": "Under GDPR Article 83, violations of basic principles or data subject rights face fines up to EUR 20 million or 4% of worldwide annual turnover. Under AI Act Article 99, Chapter III violations (high-risk requirements) face fines up to EUR 15 million or 3% of turnover, while Article 5 prohibited practices face EUR 35 million or 7%. These fines are cumulative \u2014 a single AI system that both violates GDPR data principles in its training data and fails AI Act high-risk requirements could face up to 7% (combined 4%+3%) of global turnover, or up to 11% if it also engages in prohibited practices (7%+4%).",
        "predicted_answer": "## Combined Penalty Exposure: EU AI Act + GDPR Violations\n\nBased on the provided context, the combined penalty exposure is as follows:\n\n### Individual Maximum Penalties\n\n**EU AI Act (Article 99):**\n- Prohibited AI practices: up to **EUR 35 million or 7%** of worldwide annual turnover\n- High-risk AI non-compliance: up to **EUR 15 million or 3%** of worldwide annual turnover\n- Misleading information to authorities: up to **EUR 7.5 million or 1%**\n\n**GDPR (Article 83):**\n- Basic principles violations (Art. 5-9) and data subject rights: up to **EUR 20 million or 4%** of worldwide annual turnover\n- Controller/processor obligation violations: up to **EUR 10 million or 2%**\n\n### Combined Exposure\n\nThe context explicitly states these fines are **cumulative** \u2014 a single AI system can violate both the AI Act and GDPR simultaneously. For example:\n\n- High-risk non-compliance + unlawful data processing = **3% + 4% =",
        "exact_match": 0.0,
        "f1": 1.0,
        "latency_ms": 4774.497300000803,
        "cost_usd": 0.06637199999999999,
        "total_tokens": 252804,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
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      },
      {
        "question_id": "MG-B03",
        "question": "How must a deployer using a high-risk AI system comply with both the AI Act's fundamental rights impact assessment and GDPR's data protection impact assessment?",
        "gold_answer": "The AI Act Article 27 requires public bodies and private entities providing public services to perform a Fundamental Rights Impact Assessment (FRIA) before deploying high-risk AI. GDPR Article 35 requires a DPIA when processing is likely to result in high risk to rights and freedoms. Both must identify risks, assess impact, and define mitigation measures. Deployers may combine the FRIA and DPIA to avoid duplication. The AI Act Article 26(9) specifically requires deployers to use the system's transparency documentation (Art. 13) to fulfill their GDPR DPIA obligations, creating a documented chain from provider's transparency documentation through to deployer's impact assessment.",
        "predicted_answer": "## Compliance with FRIA and DPIA Requirements\n\n### When Both Assessments Are Required\n\nA deployer must conduct a **Fundamental Rights Impact Assessment (FRIA)** under AI Act Art. 26(1a) if they are:\n- A body governed by public law or a private entity providing public services, **or**\n- A deployer of credit scoring AI (Annex III Cat. 5b) or insurance risk assessment AI (Annex III Cat. 5c)\n\nA **DPIA** under GDPR Art. 35 is required when processing is likely to result in high risk to individuals' rights and freedoms \u2014 which virtually all high-risk AI systems processing personal data will trigger.\n\n---\n\n### The Mandatory Information Pipeline\n\nAI Act Art. 26(g) **explicitly connects** the two assessments by requiring deployers to use the **provider's Art. 13 transparency documentation** to carry out the GDPR Art. 35 DPIA. This creates a mandatory flow:\n\n> **Provider (Art. 13 documentation) \u2192 Deployer (Art. 26(g) DPIA obligation) \u2192",
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      },
      {
        "question_id": "MG-B04",
        "question": "What dual obligations apply when a financial entity reports an AI-related incident under both the AI Act and DORA?",
        "gold_answer": "Under AI Act Article 73, providers must report serious AI incidents to market surveillance authorities within 15 days of establishing a causal link. Under DORA Articles 17-23, financial entities must report major ICT incidents to the financial supervisory authority using standardised templates, with classification based on clients affected, duration, geographical spread, data losses, service criticality, and economic impact. Both reporting obligations apply simultaneously \u2014 to different authorities with different timelines and different templates. The financial entity must report the same AI incident twice: once to the market surveillance authority (AI Act, 15 days) and once to the financial supervisor (DORA, based on incident classification timeline).",
        "predicted_answer": "Based on the provided context, when a financial entity reports an AI-related incident under both the AI Act and DORA, the following dual obligations apply:\n\n**Different Authorities:**\n- AI Act Art. 73: Report to the **market surveillance authority** within 15 days\n- DORA Art. 19: Report to the **financial competent authority** (e.g., ECB, BaFin) with staggered deadlines: initial notification within **4 hours**, intermediate report within **72 hours**, and final report within **1 month**\n\n**Different Timelines:**\nThe DORA deadline is significantly faster \u2014 the initial report must be filed within 4 hours of classifying the incident as major, while the AI Act allows up to 15 days.\n\n**Potentially Additional Obligations:**\nIf personal data is involved, a **GDPR Art. 33** breach notification to the DPA is also required within 72 hours, and if the entity qualifies as essential/important under NIS2, a **NIS2 Art. 23** early warning to the CSIRT is required within 24 hours \u2014 creating up to",
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      },
      {
        "question_id": "MG-B05",
        "question": "How do the AI Act's cybersecurity requirements for high-risk AI interact with NIS2 security measures for essential entities?",
        "gold_answer": "AI Act Article 15 requires high-risk AI systems to be resilient against adversarial attacks including poisoning, model evasion, and confidentiality attacks, with technical measures to prevent, detect, respond to and control such attacks. NIS2 Article 21 requires essential entities to implement: risk analysis policies, incident handling, business continuity, supply chain security, vulnerability handling, cryptography and encryption, multi-factor authentication, and cybersecurity training. When a high-risk AI system is operated by an essential entity (e.g., critical infrastructure), both sets of requirements apply: the AI system must meet AI Act Art. 15 standards AND be integrated into the entity's NIS2 cybersecurity framework (Art. 21).",
        "predicted_answer": "Based on the provided context, the AI Act's cybersecurity requirements (Article 15) and NIS2 security measures (Article 21) must be satisfied **simultaneously** for AI systems deployed by essential entities, creating a dual compliance obligation.\n\n**AI Act Article 15** requires high-risk AI systems to be resilient against adversarial attacks including data poisoning, model evasion, model inversion, and model theft, with specific technical measures such as input validation, adversarial training, output monitoring, access controls, and audit logging.\n\n**NIS2 Article 21** requires essential entities to implement cybersecurity risk management measures including risk analysis, incident handling, supply chain security, vulnerability handling, encryption, and multi-factor authentication.\n\nFor AI systems in critical infrastructure (Annex III Category 2), these frameworks **directly overlap** in several areas:\n- **Supply chain security**: Both AI Act Article 15(4) (adversarial resilience) and NIS2 Article 21(2)(d) require supply chain security measures\n- **Vulnerability management**: NIS2 Article 21(2)(e) and AI Act Article 15 both address vulnerability handling",
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      },
      {
        "question_id": "MG-B06",
        "question": "How does the GDPR's data protection by design principle interact with the AI Act's data governance requirements?",
        "gold_answer": "GDPR Article 25 requires data protection by design \u2014 implementing appropriate technical and organisational measures to integrate data-protection principles like data minimisation into processing. AI Act Article 10 requires training data to meet quality criteria with appropriate governance: design choices, data collection processes, annotation, labelling, cleaning, bias detection and mitigation. When the AI processes personal data, GDPR by-design requirements and AI Act data governance must be implemented jointly: the AI system's data pipeline must simultaneously ensure GDPR data minimisation AND AI Act data quality, which can create tension since minimisation limits data while quality often requires more diverse data.",
        "predicted_answer": "## Interaction Between GDPR Article 25 and AI Act Article 10\n\n**Direct Mapping of Requirements**\n\nGDPR Article 25 (data protection by design) and AI Act Article 10 (data governance) are explicitly aligned. As noted in the cross-reference section: \"Art. 25 GDPR by-design principles map directly to several AI Act requirements\" \u2014 specifically, data governance (Art. 10) **should implement** GDPR Art. 25 measures.\n\n**Key Areas of Overlap**\n\n- **Data minimisation tension**: AI Act Art. 10 requires training data to be \"sufficiently representative,\" while GDPR Art. 5(1)(c) requires data to be \"limited to what is necessary\" \u2014 creating a fundamental conflict resolved through synthetic data, federated learning, and differential privacy\n- **Accuracy**: Both frameworks require accurate data \u2014 GDPR Art. 5(1)(d) and AI Act Art. 10's requirement for data \"free of errors\"\n- **Purpose limitation**: Training data repurposing must have a compatible legal basis under GDPR Art. 6(4)\n\n**Practical",
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      },
      {
        "question_id": "MG-B07",
        "question": "What are the supply chain implications when a financial institution uses a third-party high-risk AI system, considering both the AI Act and DORA?",
        "gold_answer": "Under AI Act Article 25, if the financial institution modifies the AI system substantially or changes its intended purpose, it becomes the provider and assumes ALL provider obligations. Under DORA Articles 28-44, contractual arrangements with third-party ICT providers must include: clear description of services, availability and integrity provisions, exit strategies and transition plans. Critical third-party providers are subject to Union oversight. When combined: the financial entity must maintain DORA third-party oversight AND ensure the AI system meets AI Act high-risk requirements, with the risk that any modification to fit the financial use case could trigger full provider status under the AI Act.",
        "predicted_answer": "## Supply Chain Implications for Financial Institutions Using Third-Party High-Risk AI\n\n### AI Act Obligations\n\nUnder **Article 26**, the financial institution (as deployer) must:\n- Use the system per the provider's instructions\n- Assign competent human oversight\n- Monitor operation and report serious incidents within **15 days**\n- Conduct a **GDPR Art. 35 DPIA** using the provider's Art. 13 documentation\n\nIf the institution fine-tunes the model or makes a **substantial modification**, it becomes a **provider under Art. 25**, triggering full Art. 9\u201315 compliance obligations.\n\n### DORA Obligations\n\nUnder **Articles 28\u201330**, the third-party AI provider is an **ICT third-party service provider**, requiring:\n- Contracts including SLAs, data residency, **audit rights**, and exit strategies\n- Ongoing monitoring of the AI service's performance\n- If designated a **critical TPSP** by ESAs, direct oversight applies\n\n### Combined Supply Chain Structure\n\n| Actor | AI Act Role | DORA Role |\n|-------|-------------|-----------|\n|",
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      },
      {
        "question_id": "MG-B08",
        "question": "How do AI Act risk management requirements interact with DORA ICT risk management for credit scoring AI?",
        "gold_answer": "Credit scoring AI is classified as high-risk under Annex III point 5(b). Under AI Act Article 9, the provider must implement a continuous iterative risk management system throughout the system's lifecycle, covering known and foreseeable risks with residual risk kept to acceptable levels. Under DORA Articles 5-16, the financial entity must maintain an ICT risk management framework with strategies, policies, procedures and protocols. These two requirements must be integrated: the AI-specific risk management (bias, accuracy, fairness) must be embedded within the broader DORA ICT risk framework, with the AI risk management system feeding into DORA's systematic documentation and the DORA framework providing the organisational infrastructure for AI risk governance.",
        "predicted_answer": "Based on the provided context, for credit scoring AI (Annex III Category 5(b)), both frameworks apply simultaneously and overlap significantly.\n\n**Dual Obligation:** Financial entities must maintain **both** an AI Act Article 9 risk management system (covering AI-specific risks to health, safety, and fundamental rights) **and** a DORA Articles 5-7 ICT risk management framework (covering ICT infrastructure risks). The AI risk management system should function as a sub-framework within the broader DORA ICT risk management system.\n\n**Key Interactions:**\n- The AI Act Article 9 risk management must identify and mitigate AI-specific risks (bias, accuracy degradation, misuse), while DORA covers operational resilience of the underlying ICT infrastructure running the credit scoring system\n- DORA requires business continuity and manual fallback procedures for AI system failures, complementing the AI Act's human oversight requirements (Article 14)\n- Model updates and retraining must follow DORA's ICT change management procedures in addition to AI Act risk management updates\n- Post-market monitoring data (AI Act Article 72) that surfaces new risks must feed into both the",
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      },
      {
        "question_id": "MG-B09",
        "question": "What dual incident notification requirements apply when an AI system causing a cybersecurity breach affects essential infrastructure under both the AI Act and NIS2?",
        "gold_answer": "Under AI Act Article 73, the provider must report the serious incident to market surveillance authorities within 15 days of establishing a causal link. Under NIS2 Article 23, the essential entity must notify the CSIRT: within 24 hours (early warning), within 72 hours (incident notification with severity assessment), and within one month (final report with root cause). The NIS2 timelines are much more aggressive than the AI Act's 15-day deadline. Both notifications go to different authorities (market surveillance vs. CSIRT) with different templates and different information requirements. The entity must coordinate both reporting streams simultaneously.",
        "predicted_answer": "Based on the provided context, the dual incident notification requirements are:\n\n**AI Act (Article 73):** The provider must report the serious incident to the market surveillance authority **within 15 days** of becoming aware of it.\n\n**NIS2 (Article 23):** The essential entity must notify their CSIRT or competent authority in **three stages**:\n- Early warning within **24 hours**\n- Full incident notification within **72 hours**\n- Final report within **one month**\n\nThese reports go to **different authorities** \u2014 the AI Act report goes to the market surveillance authority, while NIS2 reports go to the CSIRT or NIS2 competent authority. The fastest deadline is NIS2's 24-hour early warning, which is significantly earlier than the AI Act's 15-day deadline.\n\nThe context also notes that if personal data is involved, a **GDPR Article 33** breach notification to the DPA is additionally required within 72 hours, and if the entity is also a financial entity, **DORA** requires an initial notification within just **4 hours**. Organizations are advised to build a single unified incident response process that",
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        "total_tokens": 400509,
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      },
      {
        "question_id": "MG-B10",
        "question": "How does the AI Act's conformity assessment interact with existing product safety legislation for AI-embedded products?",
        "gold_answer": "Under Article 6(1), AI systems that are safety components of products covered by Annex I Section A harmonisation legislation are subject to the same conformity assessment as the parent legislation. Under Article 43, for stand-alone high-risk AI in Annex III, providers may use internal control (Annex VI) or notified body assessment (Annex VII). For Annex I products, AI Act requirements are integrated INTO the existing product conformity assessment \u2014 not a separate process. The CE marking (Art. 48) covers both the product safety and AI compliance. Any substantial modification triggers a new conformity assessment covering both dimensions.",
        "predicted_answer": "Based on the provided context, for AI systems that are **safety components of products covered by Annex I Union harmonisation legislation** (e.g., medical devices, machinery, toys), the conformity assessment must be **integrated with existing product conformity assessment**.\n\nKey interactions:\n\n- An AI system is classified as **high-risk** when it is a safety component of an Annex I product **and** that product requires third-party conformity assessment (Article 6).\n- For such systems, **third-party assessment by a notified body is mandatory** (Annex VII), rather than self-assessment.\n- The AI conformity assessment evaluates the **entire AI system lifecycle**, including the QMS, technical documentation, risk management, and post-market monitoring.\n- Practically, the notified body for AI assessment **may differ** from the product's existing notified body (e.g., a medical device notified body), requiring **coordination between the two**.\n- Once conformity is demonstrated, the provider issues an **EU declaration of conformity** and affixes **CE marking**, which for Annex I products must satisfy **both** AI Act and sector-specific requirements (e.",
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      {
        "question_id": "MG-C01",
        "question": "A bank deploys a credit scoring AI system that experiences a data breach affecting 50,000 customers. Map out ALL regulatory obligations, timelines, authorities, and potential penalties across every applicable framework.",
        "gold_answer": "This triggers obligations under four frameworks simultaneously: (1) AI Act: Report serious AI incident to market surveillance within 15 days (Art. 73). Potential penalty: EUR 15M or 3% turnover for Chapter III violations (Art. 99). (2) GDPR: Notify supervisory authority within 72 hours and affected data subjects without undue delay (Art. 33-34). Penalty: up to EUR 20M or 4% turnover (Art. 83). (3) DORA: Report major ICT incident to financial supervisory authority with classification by impact (Art. 17-23). Penalty: per national implementation. (4) NIS2: If the bank is an essential entity, notify CSIRT within 24 hours (early warning) and 72 hours (full notification) (Art. 23). Penalty: up to EUR 10M or 2% turnover (Art. 34). Maximum cumulative penalty exposure: up to 9% of global turnover (3% AI Act + 4% GDPR + 2% NIS2). Four different authorities must be notified with four different timelines and templates.",
        "predicted_answer": "## Multi-Framework Regulatory Obligations for AI Credit Scoring Data Breach\n\n### Applicable Frameworks\n\nA bank deploying credit scoring AI falls under **all four regulatory frameworks simultaneously**: AI Act (Annex III Cat. 5b), GDPR, DORA, and NIS2 (banking = essential entity).\n\n---\n\n## INCIDENT TIMELINE & REPORTING OBLIGATIONS\n\n### T+0: Breach Detected\n- Initiate internal investigation immediately\n- Classify severity across ALL frameworks concurrently\n\n### T+4 Hours: DORA Initial Notification\n- **Authority**: Financial competent authority (e.g., ECB, BaFin, national banking supervisor)\n- **Content**: Initial notification that major ICT incident has occurred\n- **Trigger**: 50,000 customers affected likely exceeds the >10% of total clients OR >100,000 clients threshold \u2014 verify against bank's total customer base; duration and data loss criteria may independently qualify it as \"major\"\n\n### T+24 Hours: NIS2 Early Warning\n- **Authority**: National CSIRT or competent NIS2 authority\n- **Content**:",
        "exact_match": 0.0,
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        "cost_usd": 0.06641699999999995,
        "total_tokens": 442719,
        "convergence_rounds": 1,
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      },
      {
        "question_id": "MG-C02",
        "question": "An energy utility deploys AI for grid management (critical infrastructure). A third-party provider substantially modifies the model. Who bears which obligations under the AI Act, DORA, and NIS2?",
        "gold_answer": "Under AI Act Article 25, the third party that substantially modifies the system becomes the provider and assumes ALL provider obligations: risk management (Art. 9), data governance (Art. 10), documentation (Art. 11), conformity assessment (Art. 43), CE marking (Art. 48). The energy utility remains the deployer with Art. 26 obligations: human oversight, monitoring, incident reporting. Under NIS2, the energy utility as essential entity must maintain cybersecurity measures (Art. 21) and incident notification (Art. 23) regardless of who is the AI provider. Under DORA (if applicable as financial-adjacent), third-party management (Art. 28-44) requires contractual arrangements covering the modification scope. Critical infrastructure AI is high-risk under Annex III point 2, so ALL Chapter III requirements apply. The modification creates a liability gap: the original provider is no longer responsible, but the modifier may not have the original's technical infrastructure.",
        "predicted_answer": "## Obligation Analysis: Energy Utility AI Grid Management System\n\n### Threshold Classification\n\n**AI Act:** Grid management AI falls under **Annex III Category 2** (critical infrastructure \u2014 energy), making it a **high-risk AI system**.\n\n**DORA:** Energy utilities are not directly in DORA's scope (DORA covers financial entities). DORA obligations would not apply here unless the utility has financial entity characteristics.\n\n**NIS2:** Energy utilities are **essential entities** under NIS2 Article 21 (energy sector explicitly listed).\n\n---\n\n### Who Bears Which Obligations\n\n#### Third-Party Provider (Substantial Modification)\n\nUnder **AI Act Article 25**, because the third party makes a **substantial modification**, they **become the new provider** and assume full provider obligations:\n\n- **Risk management system** (Art. 9)\n- **Data governance** (Art. 10)\n- **Technical documentation** (Art. 11)\n- **Transparency/instructions for use** (Art. 13)\n- **Human oversight design** (Art. 14)\n- **Accuracy, robustness, cybersecurity** (Art",
        "exact_match": 0.0,
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        "total_tokens": 463835,
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      },
      {
        "question_id": "MG-C03",
        "question": "Design a comprehensive compliance program for a hospital deploying an AI diagnostic system, addressing the AI Act, GDPR, NIS2, and fundamental rights. What are the key tensions between these frameworks?",
        "gold_answer": "The hospital must: (1) AI Act: The diagnostic AI is high-risk (Annex III \u2014 health). Ensure provider has completed conformity assessment (Art. 43), implement human oversight with qualified medical staff (Art. 14), monitor system operation (Art. 26), report incidents (Art. 73). (2) GDPR: Conduct DPIA (Art. 35) for health data processing, ensure lawful basis (likely explicit consent or vital interests), comply with data minimisation while ensuring sufficient training data quality. (3) NIS2: Hospital as essential entity must maintain cybersecurity framework (Art. 21), report AI-related security incidents to CSIRT (Art. 23). (4) Fundamental Rights: Conduct FRIA (Art. 27) assessing discrimination risks in diagnostic outcomes. Key tensions: GDPR data minimisation vs AI Act data quality requirements (Art. 10); human oversight (Art. 14) vs clinical workflow efficiency; transparency (Art. 13) vs medical liability if clinicians over-rely on explained AI outputs; FRIA/DPIA duplication burden. Resolution: combine FRIA and DPIA, establish clinical AI governance committee, implement tiered human oversight based on diagnostic confidence levels.",
        "predicted_answer": "## Comprehensive Compliance Program: Hospital AI Diagnostic System\n\n### Regulatory Classification\n\n**AI Act Classification:** The diagnostic AI system qualifies as high-risk under **Annex III Category 5** (essential services/health) and likely also as a **safety component of a medical device** under Annex I, triggering mandatory **third-party conformity assessment** (Art. 43).\n\n**NIS2 Classification:** Hospitals are **essential entities** under NIS2 (health sector), requiring full Art. 21 cybersecurity measures and Art. 23 incident reporting.\n\n**GDPR:** Health data is **special category data** under Art. 9, triggering the highest level of data protection obligations including mandatory DPIA (Art. 35).\n\n**FRIA:** The hospital is a body governed by public law (or provides public services), making a **Fundamental Rights Impact Assessment mandatory** under Art. 26(1a).\n\n---\n\n### Program Structure\n\n#### Pillar 1: Risk Management (AI Act Art. 9 + GDPR Art. 35 + FRIA)\n\nConduct a **unified risk assessment**",
        "exact_match": 0.0,
        "f1": 0.36042402826855124,
        "latency_ms": 5830.066099999385,
        "cost_usd": 0.06641699999999995,
        "total_tokens": 484950,
        "convergence_rounds": 1,
        "active_nodes": 1,
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        "shacl_pass": 0,
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      },
      {
        "question_id": "MG-C04",
        "question": "What is the total maximum fine exposure for a tech company that develops a GPAI model with systemic risk, violates prohibited AI practices, fails GDPR data protection, and breaches NIS2 cybersecurity?",
        "gold_answer": "Maximum cumulative fine exposure across all frameworks: AI Act prohibited practices (Art. 5/99): EUR 35M or 7% of worldwide turnover. AI Act GPAI systemic risk violations (Art. 51-55/99): EUR 15M or 3% of turnover. GDPR basic principles violation (Art. 83): EUR 20M or 4% of turnover. NIS2 essential entity (Art. 34): EUR 10M or 2% of turnover. Total maximum: 16% of worldwide annual turnover (7% + 3% + 4% + 2%), or EUR 80 million (35M + 15M + 20M + 10M), whichever is higher for each individual fine. Each fine is assessed independently by different authorities under different frameworks. The ne bis in idem principle may limit overlap within the same jurisdiction, but cross-framework fines from different regulatory regimes are generally cumulative.",
        "predicted_answer": "Based on the provided context, the maximum cumulative fine exposure would be:\n\n- **AI Act Art. 99(3)**: Up to **7% of worldwide annual turnover** (prohibited AI practices \u2014 the highest tier)\n- **GDPR Art. 83(5)**: Up to **4% of worldwide annual turnover** (data protection principles violations)\n- **NIS2 Art. 34**: Up to **2% of worldwide annual turnover** (if classified as an essential entity)\n\nThis yields a **theoretical maximum of approximately 13% of global annual turnover**, plus any additional GPAI-specific penalties (Art. 99(4): 3%/\u20ac15M for GPAI obligation failures).\n\nThe context notes that AI Act Art. 99(7) requires coordination between AI Act authorities and DPAs to ensure proportionality, but the fines are **not necessarily deduplicated** \u2014 both can apply as they cover different regulatory violations. Additionally, NIS2 Art. 20(2) could expose **individual management body members to personal liability** on top of the corporate fines.",
        "exact_match": 0.0,
        "f1": 0.625,
        "latency_ms": 6401.742500002001,
        "cost_usd": 0.06630000000000003,
        "total_tokens": 506062,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0,
        "governance_accuracy": 0.0,
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      },
      {
        "question_id": "MG-C05",
        "question": "How should a multinational deploy an AI recruitment system across the EU, considering the AI Act's territorial scope, GDPR's cross-border processing rules, and varying NIS2 transposition?",
        "gold_answer": "AI Act Article 2 applies to all deployers located in the Union, regardless of provider location. Recruitment AI is high-risk under Annex III point 4. The deployer must: ensure conformity assessment from the provider (Art. 43), implement human oversight per Art. 14 (qualified HR staff in each jurisdiction), and conduct FRIA (Art. 27) for each country's specific population. GDPR requires: identifying a lead supervisory authority for cross-border processing, conducting DPIAs per Art. 35, ensuring lawful basis (likely legitimate interest or consent), and respecting Art. 22 automated decision-making rights in each jurisdiction. NIS2 transposition varies by Member State \u2014 the company must verify whether it is classified as essential/important entity in each country where it operates. Key challenge: a recruitment decision valid under one Member State's GDPR interpretation may violate another's, and the AI Act's uniform high-risk requirements may conflict with varying national employment laws.",
        "predicted_answer": "## Deploying an AI Recruitment System Across the EU: Multi-Regulatory Analysis\n\n### 1. AI Act Territorial Scope\n\nThe AI Act applies regardless of where the multinational is established \u2014 if the system is **used in the Union**, full obligations apply (Article 2). An AI recruitment system falls under **Annex III Category 4** (employment, workers management, recruitment), making it a **high-risk AI system** subject to Articles 9\u201315.\n\nKey obligations before deployment:\n- Establish a risk management system (Art. 9)\n- Implement data governance for training data (Art. 10)\n- Prepare technical documentation and instructions for use (Art. 11, 13)\n- Build human oversight mechanisms to prevent automation bias (Art. 14)\n- Complete conformity assessment and CE marking (Art. 43, 48)\n- Register in the EU database (Art. 49)\n\nNon-EU providers must designate an **EU authorized representative** (Art. 22) before placing the system on the market.\n\n---\n\n### 2. GDPR Cross-Border Processing\n\nRecruitment AI processing employee/candidate personal data",
        "exact_match": 0.0,
        "f1": 0.35555555555555557,
        "latency_ms": 5509.073999997781,
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        "total_tokens": 527177,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
        "shacl_fail": 0,
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        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-C06",
        "question": "A law enforcement agency wants to use real-time biometric identification in a public space to prevent an imminent terrorist threat. What is the complete legal pathway, conditions, and oversight requirements across all applicable frameworks?",
        "gold_answer": "AI Act Article 5 generally prohibits real-time remote biometric identification in public spaces for law enforcement. Exception: strictly necessary for prevention of specific, substantial and imminent threat to life or genuine terrorist threat. Conditions: must be authorised by judicial authority or independent administrative authority; limited in time, geographic and personal scope; subject to prior fundamental rights impact assessment; registered in the EU database. Article 14 requires verification by at least two natural persons. Biometric identification is high-risk Annex III point 1 \u2014 full Chapter III requirements apply. GDPR applies: processing of biometric data (special category, Art. 9) requires explicit legal basis, likely Art. 9(2)(g) substantial public interest. GDPR Art. 35 DPIA required before deployment. If the system is embedded in essential infrastructure, NIS2 Art. 21 cybersecurity measures apply. The complete chain: judicial authorisation \u2192 FRIA \u2192 DPIA \u2192 conformity verified \u2192 human oversight (2 persons) \u2192 time-limited deployment \u2192 incident reporting to multiple authorities.",
        "predicted_answer": "## Legal Pathway for Real-Time Biometric Identification in Public Spaces for Imminent Terrorist Threat Prevention\n\n### 1. AI Act Article 5 \u2014 Prohibited Practice Exception\n\nReal-time remote biometric identification in publicly accessible spaces for law enforcement is **generally prohibited** under Article 5(d), but an explicit exception exists for **\"prevention of genuine terrorist threat.\"**\n\n**Conditions to invoke the exception:**\n- The threat must be **genuine** (not speculative or precautionary)\n- Use must be **strictly necessary** \u2014 the exception is narrow and proportionality must be demonstrated\n- The system cannot be used beyond the scope of the specific threat\n\n---\n\n### 2. Mandatory Prior Authorization\n\nBecause this falls under a prohibited practice exception (not merely a high-risk use case), the agency must obtain **prior authorization** before deployment. This requires:\n\n- Judicial or independent administrative authority approval\n- Documentation of the specific threat justifying use\n- Defined scope: location, duration, and target parameters\n- Emergency procedures exist for time-critical situations, but authorization cannot be bypassed entirely\n\n---\n\n### 3. High-Risk AI Classification \u2014 Annex",
        "exact_match": 0.0,
        "f1": 0.39597315436241615,
        "latency_ms": 6375.002499997208,
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        "total_tokens": 548293,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
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      },
      {
        "question_id": "MG-C07",
        "question": "An insurance company uses AI for risk assessment and a data protection authority finds the training data was biased against certain ethnic groups. Trace the full enforcement cascade across AI Act, GDPR, and potential liability.",
        "gold_answer": "Insurance risk assessment AI is high-risk under Annex III point 5 (essential services). Enforcement cascade: (1) AI Act Art. 10 violation \u2014 data governance failed to detect and mitigate bias in training data. Art. 9 risk management failed to identify discrimination risk. Penalties: EUR 15M or 3% turnover. (2) GDPR Art. 5 violation \u2014 fairness principle breached through discriminatory processing. Art. 22 \u2014 automated decisions with significant effects lacked adequate safeguards. Penalties: EUR 20M or 4% turnover. (3) Fundamental Rights \u2014 Art. 27 FRIA should have identified discrimination risk against ethnic groups, linked to EU Charter Art. 21 non-discrimination. (4) Product Liability \u2014 affected individuals may bring civil claims for discriminatory insurance pricing. (5) Corrective cascade: market surveillance authority can order system withdrawal (AI Act), data protection authority can order processing halt (GDPR), and the provider must conduct new conformity assessment after fixing the bias. Combined exposure: 7% turnover (3%+4%) plus civil liability.",
        "predicted_answer": "## Full Enforcement Cascade: Biased AI Risk Assessment in Insurance\n\n### 1. AI Act Violations\n\nThe insurance risk assessment AI falls under **Annex III Category 5(c)** (risk assessment for life/health insurance), making it a **high-risk AI system** subject to full Art. 9-15 requirements.\n\n**Primary violations triggered:**\n\n- **Art. 10 (Data Governance):** The provider failed to examine \"possible biases likely to affect health and safety or fundamental rights\" and ensure training data was \"sufficiently representative.\" This is a direct violation.\n- **Art. 9 (Risk Management):** Failure to identify and mitigate foreseeable risks to fundamental rights from biased outputs.\n- **Art. 15 (Accuracy/Robustness):** System did not perform consistently or appropriately across demographic groups.\n\n**Penalty:** Art. 99(4) \u2014 up to **EUR 15 million or 3% of worldwide annual turnover**, whichever is higher.\n\nAdditionally, the deployer (insurance company) faces separate penalties under Art. 26 for failing to monitor operation and detect",
        "exact_match": 0.0,
        "f1": 0.3958333333333333,
        "latency_ms": 5612.025900001754,
        "cost_usd": 0.0664229999999999,
        "total_tokens": 569410,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
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      },
      {
        "question_id": "MG-C08",
        "question": "How does the AI Act's quality management system requirement interact with DORA's ICT governance and NIS2's cybersecurity policies for a fintech company running AI-based fraud detection?",
        "gold_answer": "The fintech faces three overlapping governance frameworks: AI Act Art. 17 QMS: requires documented strategies for regulatory compliance, design procedures, quality control, testing, data management, risk management, post-market monitoring, incident reporting, and resource management. DORA ICT Governance (Art. 5-16): requires internal governance framework for ICT risk with strategies, policies, procedures, and protocols covering all ICT assets. NIS2 Art. 21: requires policies on risk analysis, incident handling, business continuity, supply chain security, and cybersecurity training. Integration approach: the QMS should be the umbrella system incorporating both DORA ICT risk management and NIS2 cybersecurity measures as sub-frameworks. The AI-specific risk management (bias, accuracy, fairness for fraud detection) feeds into the broader ICT risk assessment, while NIS2 cybersecurity requirements map to AI Act Art. 15 (accuracy, robustness, cybersecurity). Duplication risk: without integration, the fintech maintains three separate but overlapping governance systems.",
        "predicted_answer": "## AI Act QMS + DORA ICT Governance + NIS2 Cybersecurity for AI Fraud Detection\n\n### Three Overlapping Frameworks\n\nFor a fintech running AI-based fraud detection, **three separate but overlapping obligations apply simultaneously**:\n\n**1. AI Act Article 17 (QMS)**\nFraud detection AI falls under **Annex III Category 6** (law enforcement-adjacent) or **Category 5** (essential services), making it high-risk. The QMS must include:\n- Risk management system (Art. 9)\n- Post-market monitoring (Art. 72)\n- Incident reporting procedures\n- Data governance and bias testing\n- Documentation maintained for **at least 10 years**\n\n**2. DORA Articles 5-7 (ICT Governance)**\nThe fintech must maintain an ICT risk management framework covering the AI fraud detection system as an **ICT asset**, including:\n- Business continuity and manual fallback procedures if the AI fails\n- ICT change management for model updates and retraining\n- Digital operational resilience testing (Art. 24-27), potentially including threat",
        "exact_match": 0.0,
        "f1": 0.5,
        "latency_ms": 5788.143499998114,
        "cost_usd": 0.06641399999999997,
        "total_tokens": 590524,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent",
        "shacl_pass": 0,
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      },
      {
        "question_id": "MG-C09",
        "question": "Compare and contrast the incident reporting timelines, authorities, and templates across AI Act, GDPR, DORA, and NIS2 for a single AI incident in a critical infrastructure bank.",
        "gold_answer": "For a single AI incident affecting a critical infrastructure bank, four parallel reporting obligations trigger: AI Act (Art. 73): Report to market surveillance authority within 15 days of establishing causal link. Content: AI system details, nature of incident, corrective measures. GDPR (Art. 33): Notify data protection authority within 72 hours of becoming aware. Content: nature of breach, categories of data subjects, likely consequences, mitigation measures. DORA (Art. 17-23): Report to financial supervisory authority per incident classification. Content: standardised template with clients affected, duration, geographic spread, data loss. NIS2 (Art. 23): Notify CSIRT within 24 hours (early warning), 72 hours (full notification), one month (final report). Content: suspected malicious cause, severity, impact, root cause. Comparison: NIS2 has fastest deadline (24h), GDPR next (72h), AI Act slowest (15 days). Four different authorities, four different templates, four different legal bases. The bank's incident response team needs a unified procedure mapping one event to four streams.",
        "predicted_answer": "## Incident Reporting Comparison for a Critical Infrastructure Bank AI Incident\n\n### Timeline Summary\n\n| Regulation | Initial Report | Intermediate | Final | Authority |\n|------------|---------------|--------------|-------|-----------|\n| **DORA** | 4 hours (initial notification) | 72 hours | 1 month | Financial competent authority (ECB, BaFin, etc.) |\n| **NIS2** | 24 hours (early warning) | 72 hours | 1 month | CSIRT / competent authority |\n| **GDPR** | 72 hours | N/A | Ongoing | National DPA |\n| **AI Act** | \"Immediately, no later than 15 days\" | N/A | N/A | Market surveillance authority |\n\n---\n\n### Key Differences\n\n**Fastest deadline: DORA at 4 hours** \u2014 the initial notification must be submitted before most organizations have completed root cause analysis.\n\n**Most authorities involved: Four separate bodies**, each potentially in different Member States, with different formats and classification criteria.\n\n**Triggering thresholds differ:**\n- D",
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        "f1": 0.5454545454545454,
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        "total_tokens": 611639,
        "convergence_rounds": 1,
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        "method": "single-agent",
        "shacl_pass": 0,
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      },
      {
        "question_id": "MG-C10",
        "question": "An AI regulatory sandbox participant wants to test a novel emotion recognition system in a workplace setting. What are the legal boundaries, exceptions, and framework interactions?",
        "gold_answer": "This faces a direct prohibition: AI Act Article 5 prohibits emotion recognition in workplace and educational institutions. Regulatory sandboxes (Art. 57-58) provide controlled testing environments, but Article 5 prohibitions are absolute \u2014 no sandbox exception. The only permitted emotion recognition is outside workplace/education contexts. If the participant proposes a modified scope (e.g., public safety context), then: AI Act: must operate within sandbox plan agreed with competent authority; GDPR: emotion recognition processes biometric data (special category, Art. 9) requiring explicit consent or substantial public interest; GDPR Art. 35 DPIA mandatory; fundamental rights assessment required for any biometric categorisation. Even in a sandbox, GDPR protections apply fully \u2014 sandbox does not exempt from data protection law. NIS2 applies if the sandbox operator is an essential entity. Bottom line: workplace emotion recognition cannot be tested even in a sandbox. The prohibition is not a regulatory barrier that sandboxes can bypass \u2014 it's a fundamental rights protection.",
        "predicted_answer": "## Legal Analysis: Emotion Recognition AI in Workplace Sandbox Testing\n\n### Core Prohibition \u2014 Article 5\n\nEmotion recognition in workplace settings is **explicitly prohibited** under Article 5(f) of the AI Act. This is a **categorical prohibition**, not a high-risk classification requiring additional safeguards.\n\n### Sandbox Does Not Override Prohibition\n\nCritically, the sandbox provisions **do not provide an exception**. Article 57(1) explicitly states that AI regulatory sandboxes do not affect supervisory and corrective powers, and **Article 5 prohibited practices remain prohibited even within a sandbox**. The system cannot be tested in a workplace setting regardless of sandbox participation.\n\n### Applicable Framework Interactions\n\n**AI Act:** The prohibition applies from **2 February 2025**, with penalties up to **EUR 35 million or 7% of worldwide annual turnover**.\n\n**GDPR:** Emotion recognition likely involves biometric and health data (special categories under Article 9), requiring explicit consent and a lawful basis \u2014 which would be extremely difficult to establish in an employment context given the power imbalance between employer and employee.\n\n**Fundamental Rights:** Article 5 violations intersect",
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        "convergence_rounds": 1,
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  "cognigraph-pcst-v2": {
    "summary": {
      "method": "cognigraph-pcst-v2",
      "dataset": "MultiGov-30-v3-claude-sonnet-4-6",
      "n_questions": 30,
      "avg_em": 0.0,
      "avg_f1": 0.0,
      "avg_latency_ms": 0.0,
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      "avg_tokens": 0.0,
      "avg_rounds": 0.0,
      "avg_nodes": 0.0,
      "total_cost_usd": 0.0,
      "total_latency_s": 0.0,
      "total_shacl_pass": 0,
      "total_shacl_fail": 0,
      "total_constraint_propagations": 0,
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      "avg_constrained_f1": 0.0
    },
    "questions": [
      {
        "question_id": "MG-A01",
        "question": "What AI practices are completely prohibited under the EU AI Act, and what are the penalties?",
        "gold_answer": "Article 5 prohibits: subliminal manipulation, exploitation of vulnerabilities, social scoring by public authorities, real-time remote biometric identification in public spaces (with limited exceptions for law enforcement), untargeted scraping for facial recognition databases, emotion recognition in workplace and education, and biometric categorisation using sensitive characteristics. Under Article 99, violations carry fines up to EUR 35 million or 7% of worldwide annual turnover.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
        "exact_match": 0.0,
        "f1": 0.0,
        "latency_ms": 0.0,
        "cost_usd": 0.0,
        "total_tokens": 0,
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        "active_nodes": 0,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
        "ontology_route_filtered": 0,
        "governance_accuracy": 0.0,
        "framework_fidelity": 0.0,
        "scope_adherence": 0.0,
        "cross_reference_score": 0.0,
        "constrained_f1": 0.0
      },
      {
        "question_id": "MG-A02",
        "question": "What is the complete compliance chain a provider must follow to place a high-risk AI system on the EU market?",
        "gold_answer": "The provider must follow this compliance chain: (1) Implement risk management system (Art. 9); (2) Ensure data governance for training data (Art. 10); (3) Draw up technical documentation (Art. 11); (4) Ensure record-keeping and logging (Art. 12); (5) Provide transparency and instructions for use (Art. 13); (6) Design for human oversight (Art. 14); (7) Achieve accuracy, robustness and cybersecurity (Art. 15); (8) Establish quality management system (Art. 17); (9) Complete conformity assessment (Art. 43); (10) Affix CE marking (Art. 48); (11) Register in EU database (Art. 49). Only after all steps can the system be lawfully placed on the market.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
        "exact_match": 0.0,
        "f1": 0.0,
        "latency_ms": 0.0,
        "cost_usd": 0.0,
        "total_tokens": 0,
        "convergence_rounds": 0,
        "active_nodes": 0,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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        "constrained_f1": 0.0
      },
      {
        "question_id": "MG-A03",
        "question": "What are the obligations of deployers of high-risk AI systems regarding human oversight?",
        "gold_answer": "Under Article 26, deployers must assign human oversight to natural persons with necessary competence, training and authority. Under Article 14, the system must be designed to enable individuals to: fully understand system capacities and limitations, monitor operation, remain aware of automation bias, correctly interpret outputs, decide not to use the system, override or reverse outputs, and intervene through a stop button. For biometric identification (Annex III point 1(a)), at least two natural persons must verify and confirm any action.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
        "exact_match": 0.0,
        "f1": 0.0,
        "latency_ms": 0.0,
        "cost_usd": 0.0,
        "total_tokens": 0,
        "convergence_rounds": 0,
        "active_nodes": 0,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-A04",
        "question": "What specific requirements does the EU AI Act impose on general-purpose AI models with systemic risk?",
        "gold_answer": "Under Article 51, a GPAI model is classified as having systemic risk when training computation exceeds 10^25 FLOPs. Article 55 requires such providers to: perform model evaluations with standardised protocols including adversarial testing; assess and mitigate systemic risks at Union level; keep track of and report serious incidents to the AI Office; ensure adequate cybersecurity protection. Article 52 requires all GPAI providers to maintain technical documentation, provide information to downstream providers, and have a copyright policy.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
        "exact_match": 0.0,
        "f1": 0.0,
        "latency_ms": 0.0,
        "cost_usd": 0.0,
        "total_tokens": 0,
        "convergence_rounds": 0,
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        "method": "cognigraph-pcst-v2",
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      },
      {
        "question_id": "MG-A05",
        "question": "What are the GDPR principles for processing personal data and how do they apply to AI training?",
        "gold_answer": "GDPR Article 5 requires personal data to be processed with: lawfulness, fairness and transparency; purpose limitation (collected for specified purposes only); data minimisation (adequate, relevant, limited to necessary); accuracy (kept up to date); storage limitation (not kept longer than necessary); integrity and confidentiality (appropriate security). For AI training data under Article 10 of the AI Act, these principles mean training data must comply with data minimisation and purpose limitation when processing personal data.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
        "exact_match": 0.0,
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        "latency_ms": 0.0,
        "cost_usd": 0.0,
        "total_tokens": 0,
        "convergence_rounds": 0,
        "active_nodes": 0,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 0,
        "shacl_fail": 0,
        "constraint_propagations": 0,
        "observer_redirects": 0,
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      },
      {
        "question_id": "MG-A06",
        "question": "What transparency obligations exist for AI systems that generate deepfakes or synthetic content?",
        "gold_answer": "Article 50 requires providers of AI systems generating synthetic audio, image, video or text content to ensure outputs are marked in a machine-readable format as artificially generated or manipulated. Deployers of AI systems generating deep fakes must disclose that content has been artificially generated or manipulated. For AI systems interacting directly with persons, providers must design them so persons are informed they are interacting with an AI, unless obvious from context. Exception: AI used to detect or prevent criminal offences.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
        "exact_match": 0.0,
        "f1": 0.0,
        "latency_ms": 0.0,
        "cost_usd": 0.0,
        "total_tokens": 0,
        "convergence_rounds": 0,
        "active_nodes": 0,
        "method": "cognigraph-pcst-v2",
        "shacl_pass": 0,
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      },
      {
        "question_id": "MG-A07",
        "question": "How does DORA define ICT risk management requirements for financial entities?",
        "gold_answer": "DORA Articles 5-16 require financial entities to have an internal governance and control framework ensuring effective and prudent management of ICT risk. The framework must include strategies, policies, procedures, ICT protocols and tools to protect all information assets and ICT assets including software, hardware and servers. Financial entities must identify, classify and adequately document all ICT-supported business functions.",
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      },
      {
        "question_id": "MG-A08",
        "question": "What incident notification timelines does NIS2 require for essential entities?",
        "gold_answer": "NIS2 Article 23 requires essential entities to notify CSIRT or competent authority: (a) within 24 hours \u2014 early warning indicating whether incident is suspected of being caused by unlawful or malicious acts; (b) within 72 hours \u2014 incident notification with assessment of severity and impact; (c) upon request \u2014 intermediate report; (d) within one month \u2014 final report with detailed description, root cause, and mitigation measures.",
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      },
      {
        "question_id": "MG-A09",
        "question": "What high-risk AI use cases does Annex III define for employment and workers management?",
        "gold_answer": "Annex III point 4 classifies as high-risk: AI systems for recruitment or selection of natural persons, particularly for placing targeted job advertisements, screening or filtering applications, evaluating candidates in interviews or tests. Also: AI for making decisions affecting terms of work-related relationships, promotion or termination, task allocation based on individual behaviour or personal traits, and monitoring and evaluating performance and behaviour of persons in work relationships.",
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      {
        "question_id": "MG-A10",
        "question": "What is the role and scope of AI regulatory sandboxes under the EU AI Act?",
        "gold_answer": "Articles 57-58 require Member States to ensure at least one AI regulatory sandbox is operational at national level by August 2026. Sandboxes provide a controlled environment for development, testing and validation of innovative AI systems under strict oversight. They include real-world testing conditions. Personal data may be processed in sandboxes for developing AI in the public interest, with specific safeguards. SMEs and startups have priority access. Sandbox results are considered by market surveillance authorities.",
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      {
        "question_id": "MG-B01",
        "question": "How do the EU AI Act and GDPR jointly regulate automated decision-making that affects natural persons?",
        "gold_answer": "GDPR Article 22 gives data subjects the right not to be subject to solely automated decisions with legal effects, with rights to human intervention, express their view, and contest decisions. The AI Act complements this: Article 26(7) requires deployers of high-risk AI systems making decisions about natural persons to inform them of the AI system's use. Article 13 requires sufficient transparency for deployers to interpret outputs. Article 14 mandates human oversight with override capability. Together, these create a layered protection: GDPR provides the right to contest, AI Act ensures transparency and human control over the decision process.",
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      {
        "question_id": "MG-B02",
        "question": "What are the combined penalty exposures when a high-risk AI system violates both the EU AI Act and GDPR?",
        "gold_answer": "Under GDPR Article 83, violations of basic principles or data subject rights face fines up to EUR 20 million or 4% of worldwide annual turnover. Under AI Act Article 99, Chapter III violations (high-risk requirements) face fines up to EUR 15 million or 3% of turnover, while Article 5 prohibited practices face EUR 35 million or 7%. These fines are cumulative \u2014 a single AI system that both violates GDPR data principles in its training data and fails AI Act high-risk requirements could face up to 7% (combined 4%+3%) of global turnover, or up to 11% if it also engages in prohibited practices (7%+4%).",
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      {
        "question_id": "MG-B03",
        "question": "How must a deployer using a high-risk AI system comply with both the AI Act's fundamental rights impact assessment and GDPR's data protection impact assessment?",
        "gold_answer": "The AI Act Article 27 requires public bodies and private entities providing public services to perform a Fundamental Rights Impact Assessment (FRIA) before deploying high-risk AI. GDPR Article 35 requires a DPIA when processing is likely to result in high risk to rights and freedoms. Both must identify risks, assess impact, and define mitigation measures. Deployers may combine the FRIA and DPIA to avoid duplication. The AI Act Article 26(9) specifically requires deployers to use the system's transparency documentation (Art. 13) to fulfill their GDPR DPIA obligations, creating a documented chain from provider's transparency documentation through to deployer's impact assessment.",
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      },
      {
        "question_id": "MG-B04",
        "question": "What dual obligations apply when a financial entity reports an AI-related incident under both the AI Act and DORA?",
        "gold_answer": "Under AI Act Article 73, providers must report serious AI incidents to market surveillance authorities within 15 days of establishing a causal link. Under DORA Articles 17-23, financial entities must report major ICT incidents to the financial supervisory authority using standardised templates, with classification based on clients affected, duration, geographical spread, data losses, service criticality, and economic impact. Both reporting obligations apply simultaneously \u2014 to different authorities with different timelines and different templates. The financial entity must report the same AI incident twice: once to the market surveillance authority (AI Act, 15 days) and once to the financial supervisor (DORA, based on incident classification timeline).",
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      },
      {
        "question_id": "MG-B05",
        "question": "How do the AI Act's cybersecurity requirements for high-risk AI interact with NIS2 security measures for essential entities?",
        "gold_answer": "AI Act Article 15 requires high-risk AI systems to be resilient against adversarial attacks including poisoning, model evasion, and confidentiality attacks, with technical measures to prevent, detect, respond to and control such attacks. NIS2 Article 21 requires essential entities to implement: risk analysis policies, incident handling, business continuity, supply chain security, vulnerability handling, cryptography and encryption, multi-factor authentication, and cybersecurity training. When a high-risk AI system is operated by an essential entity (e.g., critical infrastructure), both sets of requirements apply: the AI system must meet AI Act Art. 15 standards AND be integrated into the entity's NIS2 cybersecurity framework (Art. 21).",
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      },
      {
        "question_id": "MG-B06",
        "question": "How does the GDPR's data protection by design principle interact with the AI Act's data governance requirements?",
        "gold_answer": "GDPR Article 25 requires data protection by design \u2014 implementing appropriate technical and organisational measures to integrate data-protection principles like data minimisation into processing. AI Act Article 10 requires training data to meet quality criteria with appropriate governance: design choices, data collection processes, annotation, labelling, cleaning, bias detection and mitigation. When the AI processes personal data, GDPR by-design requirements and AI Act data governance must be implemented jointly: the AI system's data pipeline must simultaneously ensure GDPR data minimisation AND AI Act data quality, which can create tension since minimisation limits data while quality often requires more diverse data.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
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      },
      {
        "question_id": "MG-B07",
        "question": "What are the supply chain implications when a financial institution uses a third-party high-risk AI system, considering both the AI Act and DORA?",
        "gold_answer": "Under AI Act Article 25, if the financial institution modifies the AI system substantially or changes its intended purpose, it becomes the provider and assumes ALL provider obligations. Under DORA Articles 28-44, contractual arrangements with third-party ICT providers must include: clear description of services, availability and integrity provisions, exit strategies and transition plans. Critical third-party providers are subject to Union oversight. When combined: the financial entity must maintain DORA third-party oversight AND ensure the AI system meets AI Act high-risk requirements, with the risk that any modification to fit the financial use case could trigger full provider status under the AI Act.",
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      },
      {
        "question_id": "MG-B08",
        "question": "How do AI Act risk management requirements interact with DORA ICT risk management for credit scoring AI?",
        "gold_answer": "Credit scoring AI is classified as high-risk under Annex III point 5(b). Under AI Act Article 9, the provider must implement a continuous iterative risk management system throughout the system's lifecycle, covering known and foreseeable risks with residual risk kept to acceptable levels. Under DORA Articles 5-16, the financial entity must maintain an ICT risk management framework with strategies, policies, procedures and protocols. These two requirements must be integrated: the AI-specific risk management (bias, accuracy, fairness) must be embedded within the broader DORA ICT risk framework, with the AI risk management system feeding into DORA's systematic documentation and the DORA framework providing the organisational infrastructure for AI risk governance.",
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      },
      {
        "question_id": "MG-B09",
        "question": "What dual incident notification requirements apply when an AI system causing a cybersecurity breach affects essential infrastructure under both the AI Act and NIS2?",
        "gold_answer": "Under AI Act Article 73, the provider must report the serious incident to market surveillance authorities within 15 days of establishing a causal link. Under NIS2 Article 23, the essential entity must notify the CSIRT: within 24 hours (early warning), within 72 hours (incident notification with severity assessment), and within one month (final report with root cause). The NIS2 timelines are much more aggressive than the AI Act's 15-day deadline. Both notifications go to different authorities (market surveillance vs. CSIRT) with different templates and different information requirements. The entity must coordinate both reporting streams simultaneously.",
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      },
      {
        "question_id": "MG-B10",
        "question": "How does the AI Act's conformity assessment interact with existing product safety legislation for AI-embedded products?",
        "gold_answer": "Under Article 6(1), AI systems that are safety components of products covered by Annex I Section A harmonisation legislation are subject to the same conformity assessment as the parent legislation. Under Article 43, for stand-alone high-risk AI in Annex III, providers may use internal control (Annex VI) or notified body assessment (Annex VII). For Annex I products, AI Act requirements are integrated INTO the existing product conformity assessment \u2014 not a separate process. The CE marking (Art. 48) covers both the product safety and AI compliance. Any substantial modification triggers a new conformity assessment covering both dimensions.",
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      },
      {
        "question_id": "MG-C01",
        "question": "A bank deploys a credit scoring AI system that experiences a data breach affecting 50,000 customers. Map out ALL regulatory obligations, timelines, authorities, and potential penalties across every applicable framework.",
        "gold_answer": "This triggers obligations under four frameworks simultaneously: (1) AI Act: Report serious AI incident to market surveillance within 15 days (Art. 73). Potential penalty: EUR 15M or 3% turnover for Chapter III violations (Art. 99). (2) GDPR: Notify supervisory authority within 72 hours and affected data subjects without undue delay (Art. 33-34). Penalty: up to EUR 20M or 4% turnover (Art. 83). (3) DORA: Report major ICT incident to financial supervisory authority with classification by impact (Art. 17-23). Penalty: per national implementation. (4) NIS2: If the bank is an essential entity, notify CSIRT within 24 hours (early warning) and 72 hours (full notification) (Art. 23). Penalty: up to EUR 10M or 2% turnover (Art. 34). Maximum cumulative penalty exposure: up to 9% of global turnover (3% AI Act + 4% GDPR + 2% NIS2). Four different authorities must be notified with four different timelines and templates.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
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      },
      {
        "question_id": "MG-C02",
        "question": "An energy utility deploys AI for grid management (critical infrastructure). A third-party provider substantially modifies the model. Who bears which obligations under the AI Act, DORA, and NIS2?",
        "gold_answer": "Under AI Act Article 25, the third party that substantially modifies the system becomes the provider and assumes ALL provider obligations: risk management (Art. 9), data governance (Art. 10), documentation (Art. 11), conformity assessment (Art. 43), CE marking (Art. 48). The energy utility remains the deployer with Art. 26 obligations: human oversight, monitoring, incident reporting. Under NIS2, the energy utility as essential entity must maintain cybersecurity measures (Art. 21) and incident notification (Art. 23) regardless of who is the AI provider. Under DORA (if applicable as financial-adjacent), third-party management (Art. 28-44) requires contractual arrangements covering the modification scope. Critical infrastructure AI is high-risk under Annex III point 2, so ALL Chapter III requirements apply. The modification creates a liability gap: the original provider is no longer responsible, but the modifier may not have the original's technical infrastructure.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
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      {
        "question_id": "MG-C03",
        "question": "Design a comprehensive compliance program for a hospital deploying an AI diagnostic system, addressing the AI Act, GDPR, NIS2, and fundamental rights. What are the key tensions between these frameworks?",
        "gold_answer": "The hospital must: (1) AI Act: The diagnostic AI is high-risk (Annex III \u2014 health). Ensure provider has completed conformity assessment (Art. 43), implement human oversight with qualified medical staff (Art. 14), monitor system operation (Art. 26), report incidents (Art. 73). (2) GDPR: Conduct DPIA (Art. 35) for health data processing, ensure lawful basis (likely explicit consent or vital interests), comply with data minimisation while ensuring sufficient training data quality. (3) NIS2: Hospital as essential entity must maintain cybersecurity framework (Art. 21), report AI-related security incidents to CSIRT (Art. 23). (4) Fundamental Rights: Conduct FRIA (Art. 27) assessing discrimination risks in diagnostic outcomes. Key tensions: GDPR data minimisation vs AI Act data quality requirements (Art. 10); human oversight (Art. 14) vs clinical workflow efficiency; transparency (Art. 13) vs medical liability if clinicians over-rely on explained AI outputs; FRIA/DPIA duplication burden. Resolution: combine FRIA and DPIA, establish clinical AI governance committee, implement tiered human oversight based on diagnostic confidence levels.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
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      },
      {
        "question_id": "MG-C04",
        "question": "What is the total maximum fine exposure for a tech company that develops a GPAI model with systemic risk, violates prohibited AI practices, fails GDPR data protection, and breaches NIS2 cybersecurity?",
        "gold_answer": "Maximum cumulative fine exposure across all frameworks: AI Act prohibited practices (Art. 5/99): EUR 35M or 7% of worldwide turnover. AI Act GPAI systemic risk violations (Art. 51-55/99): EUR 15M or 3% of turnover. GDPR basic principles violation (Art. 83): EUR 20M or 4% of turnover. NIS2 essential entity (Art. 34): EUR 10M or 2% of turnover. Total maximum: 16% of worldwide annual turnover (7% + 3% + 4% + 2%), or EUR 80 million (35M + 15M + 20M + 10M), whichever is higher for each individual fine. Each fine is assessed independently by different authorities under different frameworks. The ne bis in idem principle may limit overlap within the same jurisdiction, but cross-framework fines from different regulatory regimes are generally cumulative.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
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      },
      {
        "question_id": "MG-C05",
        "question": "How should a multinational deploy an AI recruitment system across the EU, considering the AI Act's territorial scope, GDPR's cross-border processing rules, and varying NIS2 transposition?",
        "gold_answer": "AI Act Article 2 applies to all deployers located in the Union, regardless of provider location. Recruitment AI is high-risk under Annex III point 4. The deployer must: ensure conformity assessment from the provider (Art. 43), implement human oversight per Art. 14 (qualified HR staff in each jurisdiction), and conduct FRIA (Art. 27) for each country's specific population. GDPR requires: identifying a lead supervisory authority for cross-border processing, conducting DPIAs per Art. 35, ensuring lawful basis (likely legitimate interest or consent), and respecting Art. 22 automated decision-making rights in each jurisdiction. NIS2 transposition varies by Member State \u2014 the company must verify whether it is classified as essential/important entity in each country where it operates. Key challenge: a recruitment decision valid under one Member State's GDPR interpretation may violate another's, and the AI Act's uniform high-risk requirements may conflict with varying national employment laws.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
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      },
      {
        "question_id": "MG-C06",
        "question": "A law enforcement agency wants to use real-time biometric identification in a public space to prevent an imminent terrorist threat. What is the complete legal pathway, conditions, and oversight requirements across all applicable frameworks?",
        "gold_answer": "AI Act Article 5 generally prohibits real-time remote biometric identification in public spaces for law enforcement. Exception: strictly necessary for prevention of specific, substantial and imminent threat to life or genuine terrorist threat. Conditions: must be authorised by judicial authority or independent administrative authority; limited in time, geographic and personal scope; subject to prior fundamental rights impact assessment; registered in the EU database. Article 14 requires verification by at least two natural persons. Biometric identification is high-risk Annex III point 1 \u2014 full Chapter III requirements apply. GDPR applies: processing of biometric data (special category, Art. 9) requires explicit legal basis, likely Art. 9(2)(g) substantial public interest. GDPR Art. 35 DPIA required before deployment. If the system is embedded in essential infrastructure, NIS2 Art. 21 cybersecurity measures apply. The complete chain: judicial authorisation \u2192 FRIA \u2192 DPIA \u2192 conformity verified \u2192 human oversight (2 persons) \u2192 time-limited deployment \u2192 incident reporting to multiple authorities.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
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      },
      {
        "question_id": "MG-C07",
        "question": "An insurance company uses AI for risk assessment and a data protection authority finds the training data was biased against certain ethnic groups. Trace the full enforcement cascade across AI Act, GDPR, and potential liability.",
        "gold_answer": "Insurance risk assessment AI is high-risk under Annex III point 5 (essential services). Enforcement cascade: (1) AI Act Art. 10 violation \u2014 data governance failed to detect and mitigate bias in training data. Art. 9 risk management failed to identify discrimination risk. Penalties: EUR 15M or 3% turnover. (2) GDPR Art. 5 violation \u2014 fairness principle breached through discriminatory processing. Art. 22 \u2014 automated decisions with significant effects lacked adequate safeguards. Penalties: EUR 20M or 4% turnover. (3) Fundamental Rights \u2014 Art. 27 FRIA should have identified discrimination risk against ethnic groups, linked to EU Charter Art. 21 non-discrimination. (4) Product Liability \u2014 affected individuals may bring civil claims for discriminatory insurance pricing. (5) Corrective cascade: market surveillance authority can order system withdrawal (AI Act), data protection authority can order processing halt (GDPR), and the provider must conduct new conformity assessment after fixing the bias. Combined exposure: 7% turnover (3%+4%) plus civil liability.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
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      },
      {
        "question_id": "MG-C08",
        "question": "How does the AI Act's quality management system requirement interact with DORA's ICT governance and NIS2's cybersecurity policies for a fintech company running AI-based fraud detection?",
        "gold_answer": "The fintech faces three overlapping governance frameworks: AI Act Art. 17 QMS: requires documented strategies for regulatory compliance, design procedures, quality control, testing, data management, risk management, post-market monitoring, incident reporting, and resource management. DORA ICT Governance (Art. 5-16): requires internal governance framework for ICT risk with strategies, policies, procedures, and protocols covering all ICT assets. NIS2 Art. 21: requires policies on risk analysis, incident handling, business continuity, supply chain security, and cybersecurity training. Integration approach: the QMS should be the umbrella system incorporating both DORA ICT risk management and NIS2 cybersecurity measures as sub-frameworks. The AI-specific risk management (bias, accuracy, fairness for fraud detection) feeds into the broader ICT risk assessment, while NIS2 cybersecurity requirements map to AI Act Art. 15 (accuracy, robustness, cybersecurity). Duplication risk: without integration, the fintech maintains three separate but overlapping governance systems.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
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      },
      {
        "question_id": "MG-C09",
        "question": "Compare and contrast the incident reporting timelines, authorities, and templates across AI Act, GDPR, DORA, and NIS2 for a single AI incident in a critical infrastructure bank.",
        "gold_answer": "For a single AI incident affecting a critical infrastructure bank, four parallel reporting obligations trigger: AI Act (Art. 73): Report to market surveillance authority within 15 days of establishing causal link. Content: AI system details, nature of incident, corrective measures. GDPR (Art. 33): Notify data protection authority within 72 hours of becoming aware. Content: nature of breach, categories of data subjects, likely consequences, mitigation measures. DORA (Art. 17-23): Report to financial supervisory authority per incident classification. Content: standardised template with clients affected, duration, geographic spread, data loss. NIS2 (Art. 23): Notify CSIRT within 24 hours (early warning), 72 hours (full notification), one month (final report). Content: suspected malicious cause, severity, impact, root cause. Comparison: NIS2 has fastest deadline (24h), GDPR next (72h), AI Act slowest (15 days). Four different authorities, four different templates, four different legal bases. The bank's incident response team needs a unified procedure mapping one event to four streams.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
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      },
      {
        "question_id": "MG-C10",
        "question": "An AI regulatory sandbox participant wants to test a novel emotion recognition system in a workplace setting. What are the legal boundaries, exceptions, and framework interactions?",
        "gold_answer": "This faces a direct prohibition: AI Act Article 5 prohibits emotion recognition in workplace and educational institutions. Regulatory sandboxes (Art. 57-58) provide controlled testing environments, but Article 5 prohibitions are absolute \u2014 no sandbox exception. The only permitted emotion recognition is outside workplace/education contexts. If the participant proposes a modified scope (e.g., public safety context), then: AI Act: must operate within sandbox plan agreed with competent authority; GDPR: emotion recognition processes biometric data (special category, Art. 9) requiring explicit consent or substantial public interest; GDPR Art. 35 DPIA mandatory; fundamental rights assessment required for any biometric categorisation. Even in a sandbox, GDPR protections apply fully \u2014 sandbox does not exempt from data protection law. NIS2 applies if the sandbox operator is an essential entity. Bottom line: workplace emotion recognition cannot be tested even in a sandbox. The prohibition is not a regulatory barrier that sandboxes can bypass \u2014 it's a fundamental rights protection.",
        "predicted_answer": "ERROR: [Errno 22] Invalid argument",
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    ]
  }
}