# Requirements Analysis

## Functional Requirements
- Create trusted baseline of runtime state (`Baseline.create()` in `cynapse/core/baseline.py`).
- Hash and track function bytecode (`BytecodeAnalyzer`).
- Track module load/modify/remove events (`ModuleTracker`).
- Monitor `sys.meta_path` for import hook changes (`ImportHookMonitor`).
- Detect monkey-patching of functions (`MonkeyPatchDetector`).
- Provide configurable responses and callback hooks (`Monitor.on_tamper()`).
- Expose high-level APIs: decorators, context manager, builder, async monitor.

## Non-functional Requirements
- Performance: <2% CPU when idle; minimal memory overhead (see `README.md`).
- Cross-platform: Linux/Windows/macOS (`cynapse/platform/`).
- Reliability: resilient background loop with graceful error handling.
- Usability: simple integration into existing apps; framework support.
- Observability: status access via `Monitor.get_status()`; optional psutil.

## User requirements and personas
- Developers: add `@protect_function` or use `with Monitor(...)` with little friction.
- Security engineers: define tamper callbacks and enforce response policies.
- SRE/Platform teams: monitor status metrics and tune intervals/whitelists.

## System constraints
- Python 3.8+; pure stdlib core; optional extras (`blake3`, frameworks, `psutil`).
- Some OS memory features may require elevated privileges.
- Dynamic code (JIT/hot reload) may need whitelisting to avoid false positives.

## Use case diagrams or user stories
- Protect a critical function:
  - As a developer, I add `@protect_function` to `process_payment()` so that any runtime modification is detected and optionally auto-restored.
- Monitor a web app:
  - As a platform engineer, I initialize `FlaskMonitor` to protect admin/payment endpoints and monitor import hooks.
- Async services:
  - As a developer of an asyncio service, I use `AsyncMonitor` to protect async endpoints.

References: `tests/`, `examples/`, `README.md`, `cynapse/decorators.py`.
