12 AI Security Framework

Mandatory adversarial threat modeling for all AI systems prior to design freeze. Must map attack vectors including prompt injection, data poisoning, model inversion, and membership inference.

All training and inference data must carry cryptographic lineage tracking. Immutable audit trails required from ingestion to model output. Synthetic data must be explicitly tagged and validated.

Every deployed model requires a living governance registry: versioning, hyperparameters, training corpus hashes, evaluation metrics, and known failure modes. Quarterly third-party audits mandatory.

Models must pass standardized red-team benchmarks against evasion attacks, jailbreaks, and prompt manipulation. Defense-in-depth includes input sanitization, output filtering, and runtime guardrails.

Least-privilege access enforced across AI toolchains. API keys, model weights, and fine-tuning datasets require dynamic credential rotation, hardware-backed encryption, and just-in-time access provisioning.

Shift-left security integrated into MLOps pipelines. Automated SAST/DAST for training scripts, dependency scanning for model libraries, and signed commit verification for all model artifacts.

Continuous telemetry collection for inference latency, drift detection, token usage patterns, and behavioral anomalies. Automated circuit breakers trigger on threshold violations.

Pre-deployment fairness audits across protected attributes. Real-time output moderation for harmful, discriminatory, or legally non-compliant responses. Alignment scoring integrated into CI/CD.

Strict vetting of third-party models, APIs, and open-source components. SBOM generation for AI stacks. Vulnerability disclosure SLAs enforced with all AI vendors.

Dedicated AI incident playbooks covering model compromise, data leakage, and autonomous drift. One-click model rollback, traffic shifting, and forensic snapshot capabilities required.

High-stakes AI decisions (financial, medical, defense, legal) require mandatory human validation gates. Confidence thresholds and escalation paths documented per use-case.

Automated policy enforcement against NIST AI RMF, EU AI Act, ISO 42001, and internal standards. Continuous benchmarking against industry red-team suites and regulatory updates.