AI Risk Mitigation

Risk Strategy

AI Risk Taxonomy: Define a practical taxonomy covering privacy, security, bias, hallucinations, safety, IP, and regulatory exposure.
Risk Appetite Definition: Establish risk tolerance by use case class (internal, customer-facing, regulated, high-impact decisions).
Risk Tiering Framework: Classify systems into tiers with required controls, approvals, and evidence expectations.
Control Library Design: Create a standardized set of controls mapped to risk tiers to reduce one-off debates.

AI Governance Forums: Establish forums for approvals, escalations, and exception handling with clear decision rights.
Model Approval Process: Define entry/exit criteria for experimentation, pilot, and production releases.
Accountability Model: Define who is accountable for outcomes, incidents, and risk acceptance (business, IT, risk, legal).
Documentation Standards: Require consistent documentation for intended use, limitations, testing evidence, and monitoring.

Acceptable Use Policy: Define permitted and prohibited uses of AI, including data handling and restricted content categories.
Data Handling Standards: Establish standards for retention, masking, redaction, and “permission to learn” boundaries.
Prompt and Output Standards: Define standards for system prompts, refusal behavior, citations, and user disclosures.
Third-Party Usage Standards: Define rules for external tools/models, including data sharing, logging, and contractual protections.

PII Discovery and Classification: Identify sensitive data in training, retrieval, logs, and prompts to prevent accidental exposure.
Data Minimization Controls: Limit data to what is necessary via filtering, aggregation, and de-identification.
Consent and Purpose Controls: Ensure data is used only for approved purposes with documented legal basis where required.
Retention and Deletion Controls: Implement retention limits and deletion workflows for prompts, logs, embeddings, and training artifacts.

Threat Modeling: Threat-model AI systems including retrieval layers, tool use, and provider integrations.
Access and Entitlement Controls: Ensure identity-based access control for data, tools, and models with least privilege.
Secure Logging Strategy: Balance observability with sensitive data protection in logs and traces.
Supply Chain Security: Manage dependency vulnerabilities, container security, and provider security posture.

Intended Use Definition: Define intended use, out-of-scope uses, and known limitations to prevent misuse.
Bias and Fairness Assessment: Evaluate bias risks, disparate impact, and mitigation plans where decisions affect people.
Explainability Requirements: Define when explanations are required, what constitutes acceptable evidence, and how it is delivered.
Performance Evidence Pack: Produce evidence of quality across segments, edge cases, and relevant scenarios for approvals.

Hallucination Mitigation: Implement grounding, citation, refusal behaviors, and uncertainty handling.
Prompt Injection Defense: Protect tool calls and instruction hierarchy from malicious content and hidden instructions.
Content Safety Filters: Implement filters for disallowed content, unsafe instructions, and sensitive topics.
IP and Confidentiality Controls: Reduce leakage risk for proprietary content and define rules for external sharing and training.

Vendor Due Diligence: Assess vendor security, privacy posture, reliability, and model update practices.
Contractual Guardrails: Define clauses for data rights, retention, indemnities, auditability, and breach notification.
Model Update Governance: Control how vendor model updates are adopted with testing and approval gates.
Exit and Portability Plan: Ensure portability of prompts, evals, and data so the organization can switch vendors if needed.

Adversarial Testing: Test against jailbreaks, injection, data exfiltration attempts, and unsafe tool use.
Scenario-Based Risk Testing: Test realistic workflows including high-risk decisions and edge cases.
Regression Safety Testing: Detect safety regressions after prompt/model/index changes.
Independent Review: Enable periodic independent reviews of high-risk systems for control effectiveness.

Safety Event Monitoring: Monitor policy violations, near misses, and blocked actions with escalation triggers.
Model Drift Monitoring: Track performance changes and shifts that could increase harm or error rates.
Incident Playbooks: Establish playbooks for leakage events, harmful outputs, and provider outages.
Post-Incident Remediation: Convert incidents into control improvements, updated policies, and additional testing.

Role-Based Training: Train users, builders, and approvers on safe usage, verification habits, and escalation expectations.
Audit Evidence Management: Maintain evidence packs (approvals, tests, monitoring) that satisfy internal and external audits.
Compliance Mapping: Map controls to relevant regulatory and internal policy requirements to reduce gaps.
Continuous Control Improvement: Establish a cadence to update controls as regulations, threats, and technology evolve.