Risk Management

Risk Management

Risk	Impact	Mitigation
LLM output is incorrect or unsafe to execute.	Bad transformations, misleading SQL, or broken training code.	Stream workflow steps, keep generated code visible, require approval gates, validate schemas, reject unsafe SQL, and support interrupt/retry.
Python execution can exhaust host resources.	Local or deployed service instability.	Use Docker runtime limits for memory, CPU, timeout, tmpfs, and isolated workspaces.
Deployment prediction logs may contain sensitive data.	Privacy and compliance exposure.	Treat logs as protected data, enforce deployment ownership/API keys, and restrict access in production.
Database migrations drift from deployed backend code.	Runtime failures or missing persistence.	Run npm run db:migrate during setup/deploy and keep migrations idempotent.
Benchmark data is misplaced or committed incorrectly.	Bloated repo or unreproducible evaluation.	Follow testing/benchmarks storage rules and keep large public staged bytes out of git.
Auth or ownership checks are bypassed in new routes.	Cross-project data access.	Use route-level ownership middleware, write route tests, and review all new API surfaces.
Frontend state persistence becomes stale after schema changes.	Broken UI state or confusing workspace behavior.	Version persisted state, test stores, and provide recovery/reinitialization paths.
Docker network policy blocks needed packages or allows too much access.	Failed workflows or security exposure.	Configure EXECUTION_NETWORK deliberately per environment and document package/runtime expectations.
LLM provider latency/cost spikes.	Slow UX and cost overruns.	Use model-specific timeouts, stream progress, keep deterministic fallbacks where possible, and monitor usage.
Final documentation diverges from code.	Onboarding and maintenance errors.	Keep wiki pages tied to repo paths, commands, and current route/service structure.