AI compliance is the set of decisions, controls, and practices that keep your AI systems aligned with applicable laws, regulations, and internal policies so you can deploy responsibly and prove it.
Why it matters: regulations, customer contracts, and audits increasingly require documented evidence that your AI is lawful, safe, and well-governed. Not just functional.
AI Compliance vs. Governance vs. Security
- Compliance: Demonstrably meeting external obligations (laws, standards, contracts) and internal policies. Evidence-centric.
- Governance: How you set strategy, accountability, and lifecycle controls for AI (who decides what, when, and how). Framework-centric.
- Security: Protecting data, models, and pipelines from threats (access controls, monitoring, incident response). Tooling-centric; often automated by trust platforms.
These overlap. Effective programs map governance decisions to specific controls and produce evidence to satisfy compliance.
Where Compliance Breaks Down
- Shifting rules & timelines (e.g., EU AI Act staged obligations through 2027).
- Evidence gaps between policy and what’s actually logged/tested in pipelines. (Auditors want artifacts, not promises.)
- Model changes without change control (drift, retraining, prompts) that outpace documentation.
- Third-party & GPAI exposure with unclear responsibilities.
Key Regulations & Standards You Should Track
- EU AI Act
- Entered into force in 2024; GPAI obligations apply August 2, 2025; high-risk obligations August 2, 2026; broad effectiveness by 2027. No pause is expected per the European Commission.
- NIST AI Risk Management Framework (AI RMF 1.0)
- Four core functions: GOVERN, MAP, MEASURE, MANAGE; widely used to structure controls and evidence.
- ISO/IEC 42001 (AI Management Systems)
- Requirements for an AI Management System (AIMS) covering lifecycle, risk, and continuous improvement; includes AI-specific controls.
- Sector & privacy overlays
- GDPR, HIPAA, SOC 2, PCI, and contractual obligations still apply to AI data, pipelines, and outputs. (Compliance programs should map these to model-specific risks.)
Swept’s AI Compliance Playbook
Align to frameworks, generate evidence automatically, and keep pace with change.
- Profile & Scope
- Inventory AI use cases, data classes, models (first-party & vendor), risk levels, intended use, and impacted users. Map to EU AI Act roles.
- Control Baseline
- Start with NIST AI RMF + ISO/IEC 42001 control set; add privacy & sector overlays.
- Test & Monitor
- Synthetic red-teaming, misuse/abuse tests, bias/fairness checks, explainability probes, and safety policies—with versioned artifacts.
- Evidence & Audit Trail
- Continuous logging of prompts, responses, datasets, model versions, approvals, incidents, remediations—ready for assessments.
- Release Gates
- “No-go” thresholds for high-risk use, human-in-the-loop requirements, sign-offs, and rollback plans tied to compliance posture.
- Vendor & GPAI Management
- Intake questionnaires, transparency docs, license/copyright attestations, and model card validation.
Real-World Use Cases
- Healthcare: Document HIPAA-aligned handling, PHI minimization, bias testing, and human oversight for clinical decision support.
- Financial services: Governance workflows for model risk (explainability, fairness, record-keeping) aligned to RMF/ISO controls.
- SaaS vendors: Customer-facing compliance packets (control mappings, test results, incident playbooks) to accelerate security reviews.
How Swept Makes Compliance Easier
- Framework-first mappings: NIST AI RMF and ISO/IEC 42001 baselines with pre-built control sets and evidence templates.
- Automated testing & telemetry: Synthetic safety/bias tests, drift & change detection, and full lineage tracking produce auditor-ready artifacts.
- Compliance-grade documentation: One-click exports for assessments, RFPs, and trust portals; integrates with trust/compliance platforms to streamline reviews.
Turn your AI program into measurable evidence—mapped to the standards your buyers and regulators expect.
