Why this matters
As an Applied Scientist, you move models into products. Risk Mitigation Planning turns “we hope it’s safe” into concrete, testable controls. Typical on-the-job tasks include:
- Scoping harms from a new feature using an LLM or classifier.
- Estimating severity and likelihood of failures (e.g., bias, privacy leaks, toxicity, security misuse).
- Choosing and validating mitigations (data, model, inference-time, human-in-the-loop, monitoring).
- Defining release gates, incident response, and rollback paths.
Progress note
The quick test is available to everyone. Only logged-in users get saved progress.
Concept explained simply
Risk Mitigation Planning is a short document and process that answers: What can go wrong, how bad is it, how likely is it, what will we do to prevent/detect/respond, and who owns it.
Mental model
- Map risks → Rate (Severity × Likelihood) → Mitigate → Verify → Monitor → Respond.
- Think in three layers: Prevent, Detect, Respond.
- Make it auditable: a risk register plus evidence of tests and owners.
Scales that work in practice
- Severity (S): 1=minor, 2=annoying, 3=harmful, 4=serious, 5=critical.
- Likelihood (L): 1=rare, 2=unlikely, 3=possible, 4=likely, 5=frequent.
- Risk score R = S × L. Focus on R ≥ 12 first.
Step-by-step: Build a Risk Mitigation Plan
- Define context
- Purpose, users, environments, constraints.
- High-risk use cases and out-of-scope misuse.
- Identify harms and stakeholders
- Harms: safety, bias/discrimination, privacy/security, misinformation, IP, legal/regulatory, reputation.
- Stakeholders: end users, impacted groups, employees, partners, the public.
- Estimate S × L and prioritize
- Rate each risk with S and L; compute R and sort.
- Flag unknowns to investigate (data or tests needed).
- Choose controls (Prevent–Detect–Respond)
- Prevent: data curation, debiasing, safe decoding, adversarial training, policy-aligned prompts, rate limits.
- Detect: validation sets, red-team probes, classifiers/filters, canary prompts, drift and anomaly monitoring.
- Respond: human escalation, kill-switch, rollback, incident comms, model/key rotation.
- Define release gates and residual risk
- Acceptance criteria: e.g., toxicity below X%, equal opportunity difference ≤ Y, privacy leakage ≤ Z.
- Document residual risk and justification for launch.
- Assign ownership and evidence
- Owner (accountable), reviewers (safety/legal), approver (product/exec).
- Evidence: test reports, metrics, sign-offs, monitoring dashboard screenshots.
- Plan monitoring and incidents
- Live metrics, alert thresholds, on-call rotation.
- Runbooks for rollback, user messaging, and fixes.
Control library (quick picker)
- Data: reweighing, stratified sampling, de-duplication, PII scrubbing, synthetic counterfactuals.
- Model: constraint-aware training, adversarial training, calibration, thresholding, ensemble/abstain.
- Inference-time: content filters, safety classifiers, tool permission allowlists, safe decoding, guardrails, rate limits.
- Human-in-the-loop: review queues, escalation policies, denial reasons, feedback loops.
- Monitoring/response: drift, performance SLOs, audit logs, rollback, key rotation.
Worked examples
1) LLM support assistant producing toxic replies
Risk: Toxic or harassing output to customers (S=4, L=3 → R=12).
Mitigations (Prevent): instruct with policy-aligned system prompt; safe decoding (top-p, temperature caps).
(Detect): toxicity classifier on outputs; canary prompts; red-team set at each release.
(Respond): auto-block + human escalation; rate-limit suspicious sessions; rollback model.
Gates: Toxicity < 0.5% on eval; 0 PII leakage in tests; alert if >0.2% in prod 24h.
2) Credit eligibility model fairness
Risk: Systematic denials for protected groups (S=5, L=2 → R=10).
Mitigations (Prevent): reweigh training data; fairness-aware thresholding; model cards with subgroup metrics.
(Detect): equal opportunity difference and demographic parity on validation; shadow evaluation pre-launch; periodic fairness monitoring in prod.
(Respond): human review for borderline cases; retraining with new data; explainability reports to audit teams.
Gates: EOD ≤ 0.05 absolute; calibration error ≤ 2% for all major subgroups.
3) Prompt injection on LLM with tools
Risk: Injection causes unauthorized tool calls or data exfiltration (S=5, L=3 → R=15).
Mitigations (Prevent): tool allowlist/denylist; explicit tool-use deliberation; strong system prompts; sensitive-data masking.
(Detect): policy classifier on input/output; anomaly detection on tool call patterns; red-team with injection patterns.
(Respond): revoke/rotate API keys; disable affected tools; incident playbook and user notification if needed.
Gates: 0 successful exfiltration across 1k red-team injections; tool-call false positive rate ≤ 1%.
4) Medical triage model false negatives
Risk: Missed urgent cases (S=5, L=2 → R=10).
Mitigations (Prevent): set high-recall threshold; abstain on low confidence; escalate to clinician review.
(Detect): continuous calibration checks; subgroup sensitivity.
(Respond): immediate rollback if recall < target; clinician double-check for flagged drift periods.
Gates: Recall ≥ 0.98 on critical class; abstain rate ≤ 8% overall; weekly drift review.
Practice exercises (do these now)
These mirror the exercises below. Use the checklists to self-verify.
Exercise 1: Create a mini risk register
- ☐ Define feature, users, context
- ☐ List at least 3 risks with S, L, R
- ☐ Add one Prevent, Detect, Respond control per risk
- ☐ Write a clear release gate and residual-risk note
Exercise 2: Drift and incident runbook
- ☐ Choose three live metrics and thresholds
- ☐ Define an alerting rule and on-call owner
- ☐ Write rollback steps and user messaging
- ☐ Add a follow-up learning loop for a fix
Common mistakes and how to self-check
- Vague risks → Rewrite into concrete failure modes with stakeholders affected.
- Controls without evidence → Add a test, a metric, and a pass/fail gate for each control.
- Over-index on prevention → Ensure you also have detection and response with owners.
- Ignoring subgroups → Always include subgroup metrics and thresholds.
- No rollback → Add a reversible deployment plan and kill-switch.
Self-check prompts
- If this shipped today, what could go wrong tomorrow morning, and who would know?
- Can you show a screenshot or report proving each gate passed?
- Who wakes up at 2am and what exactly do they do first?
Practical projects
- Build a risk register and mitigation plan for a product description generator. Include toxicity, hallucination, and IP risks.
- Ship a shadow deployment of a classifier with drift monitors and a rollback runbook. Simulate drift and execute the plan.
- Create a fairness evaluation harness with subgroup dashboards and gate criteria. Run it on two model variants and choose one.
Learning path
- Start: Risk identification and S×L scoring on one feature.
- Next: Implement at least one control in each layer (Prevent, Detect, Respond).
- Then: Add gates and monitoring; run a red-team and incident drill.
- Finally: Document residual risk and sign-offs; iterate after launch.
Who this is for
- Applied Scientists building or integrating ML/LLM features.
- Data Scientists moving prototypes to production.
- Engineers and PMs collaborating on responsible AI launches.
Prerequisites
- Basic ML model training and evaluation.
- Comfort with common metrics (precision/recall, calibration).
- Awareness of privacy, security, and fairness concepts.
Next steps
- Complete the exercises and compare with the provided solutions.
- Take the quick test to verify your understanding.
- Apply this planning template to your next model change.
Mini challenge
In 15 minutes, write a one-page plan for a summarization feature used by customer agents. Include: top 3 risks, one control per layer, gates, and the first three steps in your incident runbook. Keep it specific and testable.