Security & Compliance Standards for AI Systems

Binding EU law classifying AI systems by risk: Unacceptable (banned, e.g., social scoring), High (regulated: healthcare, hiring, critical infrastructure — requires conformity assessment, CE marking, human oversight, explainability, audit logs retained 10 years), Limited (transparency obligations), Minimal (unregulated). GPAI (General Purpose AI) models with >10²⁵ FLOPs training compute face additional systemic risk obligations. Penalties: up to €35M or 7% of global revenue.

US voluntary framework for managing AI risk across four functions: GOVERN (policies, roles, culture), MAP (risk identification and context), MEASURE (risk analysis and evaluation), MANAGE (risk treatment and monitoring). Closely aligned with NIST CSF 2.0. Required for US federal AI procurement under EO 14110. Not legally binding but de facto standard for US government contractors.

Certifiable management system standard for AI (parallel to ISO 27001 for infosec). Requires an AI policy, risk assessment process, impact assessments for high-risk uses, and continual improvement cycle. Provides third-party certification pathway. Particularly valued for B2B enterprise procurement and demonstrating due diligence. Compatible with and maps to EU AI Act requirements.

Traditional SOC 2 Trust Service Criteria (Security, Availability, Confidentiality, Processing Integrity, Privacy) extended with AI-specific controls: training data integrity, model versioning and change management, bias monitoring, explainability for automated decisions, and AI incident response. Growing demand from enterprise SaaS customers deploying AI features.

Maintain cryptographic hashes and provenance records for all training datasets. Enforce data access controls with column-level RBAC. Scan ingested data for PII (using tools like Presidio or AWS Comprehend) and apply pseudonymization or anonymization before training. Log all data access for audit trails. Apply differential privacy (ε-DP) for models trained on sensitive data.

Conduct AI-specific threat modeling using STRIDE adapted for ML: Spoofing (adversarial inputs), Tampering (data/model poisoning), Repudiation (missing audit logs), Information disclosure (model inversion), Denial of service (model extraction), Elevation of privilege (prompt injection bypassing access controls). Conduct structured red-teaming sessions before production deployment — especially for LLM features.

Run training workloads in isolated compute environments (VPC, no internet access). Use immutable container images with pinned base layers. Scan all pip dependencies with pip-audit or Safety. Enforce model artifact signing (cosign) and store checksums in a tamper-evident log. Restrict write access to feature stores and model registries to CI/CD service accounts only.

Evaluate models on fairness metrics disaggregated by protected attributes before every release. Use IBM Fairness 360 or Google's What-If Tool for audit. Document evaluation results in a Model Card. For EU AI Act high-risk systems, conduct a formal Fundamental Rights Impact Assessment (FRIA). Log all evaluation results and retain for the duration required by applicable regulation.

Deploy inference behind an API gateway with authentication, rate limiting, and query anomaly detection. Apply input validation (schema enforcement, length limits, character filtering for LLMs). Implement output scanning for PII, harmful content, and hallucination indicators. For LLMs: enforce system prompt isolation and use a privilege-separation architecture that prevents user-supplied content from accessing system-level context.

Monitor for data drift (feature distribution shift signals dataset poisoning), prediction drift (accuracy degradation signals adversarial activity), and anomalous query patterns (high-volume similar queries signals extraction attempts). Define AI-specific incident playbooks: model rollback procedure, customer notification triggers, regulator reporting requirements under EU AI Act Article 62 (serious incident reporting within 15 days).

Catalog every AI system in production or development. Classify each by EU AI Act risk tier and NIST AI RMF impact category. Document use case, data inputs, decision type (advisory vs. binding), affected population, and applicable regulations. This inventory is the foundation artifact for every subsequent compliance step.

Draft an AI Policy (ISO 42001 Clause 5.2): objectives, scope, roles (AI Owner, Data Steward, CISO, DPO). Establish an AI Risk Committee with defined meeting cadence and escalation paths. Define the AI lifecycle process: ideation → data assessment → development → red-teaming → staged rollout → monitoring → decommission.

For each high-risk system, conduct an AI Risk Assessment (likelihood × impact for each threat category) and a Fundamental Rights Impact Assessment (FRIA). Document residual risks and accepted risk register. Map risks to technical controls. For GDPR-sensitive systems, conduct or update the Data Protection Impact Assessment (DPIA).

Implement the prioritized control set from the risk assessment: data provenance logging, model versioning with cryptographic signing, audit log infrastructure (immutable append-only log, 10-year retention for EU high-risk), input/output validation, bias monitoring dashboards, and explainability APIs. Automate controls in CI/CD where possible.

Produce a Model Card for every production model (intended use, out-of-scope uses, training data summary, evaluation results by subgroup, known limitations, ethical considerations). Produce Technical Documentation per EU AI Act Annex IV: system description, design specifications, validation datasets, performance metrics, and human oversight measures.

Assemble the compliance dossier: AI inventory, policies, risk assessments, FRIA/DPIA, model cards, technical documentation, audit logs, testing results. Draft AI Incident Response Playbook: detection criteria, severity levels, internal escalation, customer notification triggers, and regulatory notification procedure (EU AI Act Article 62: serious incidents within 15 days). Run a tabletop exercise.