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Policy & ComplianceOverview — AI Policy & Compliance

AI Policy & Compliance

AI policy is how your organization turns expectations into enforceable operating rules. It covers the policies, review steps, and technical controls that keep AI systems auditable, explainable, and aligned to regulatory requirements.

VeriProof is the production evidence layer for that work. It makes each decision traceable, each record tamper-evident, and each compliance obligation easier to defend with real operating data.

This section covers the policy frameworks and compliance guides most commonly used with large language model systems. If you’re looking for a starting point, begin with EU AI Act overview or NIST AI RMF overview.


What Is an AI Policy Program?

A complete AI policy program covers the full lifecycle of an AI system:

PhaseActivitiesVeriProof role
DesignRisk classification, intended use documentation, bias assessment, data governanceDocumentation feed — provides production baselines for comparison
DevelopmentModel selection, training data documentation, test evaluationOut of scope for VeriProof
DeploymentConfiguration management, access controls, deployment documentationDeployment context signing; infrastructure security
ProductionContinuous monitoring, drift detection, incident responseCore VeriProof use case
ReviewPeriodic audit, compliance evidence generation, model update assessmentCompliance evidence export; time-machine analysis
RetirementDecommissioning records, data deletionGDPR cryptographic erasure; records retention

VeriProof is primarily a production and review tool. It does not replace design-time policy work. It provides the operating evidence that makes those decisions verifiable after deployment.


Regulatory Landscape

The major frameworks and regulations applicable to AI systems in 2026:

FrameworkJurisdictionTypeScope
EU AI ActEuropean UnionMandatory regulationAll AI systems placed on the EU market; high-risk systems have specific requirements
NIST AI RMFUnited StatesVoluntary frameworkAll organisations; adoption increasing in regulated industries and federal contracting
HIPAAUnited StatesMandatory regulationAI systems processing Protected Health Information
GDPREU + UK + 100+ countriesMandatory regulationData protection for AI systems processing personal data of EU/UK individuals
SOC 2US-originated, global acceptanceVoluntary audit standardService organisations handling customer data

VeriProof Policy Capabilities

Immutable Audit Trail

Every AI decision captured through VeriProof is stored with a blockchain-anchored Merkle proof. This means no record can be silently altered — any tampering would break the cryptographic chain and be immediately detectable. This is the foundation that makes all other governance evidence trustworthy.

Policy Scoring

You define what constitutes a well-governed decision for your use case — confidence thresholds, refusal rates, tone indicators, fairness signals — and VeriProof scores every session against those criteria automatically. This produces a continuous, quantitative policy signal rather than a periodic sampling exercise.

Compliance Evidence Export

On demand, VeriProof generates structured evidence packages for specific regulatory frameworks. These packages include session records, proof data, policy scores, and attestation material in a form suitable for auditor review.

GDPR Cryptographic Erasure

The session record lifecycle is designed to satisfy GDPR right-to-erasure requirements without disrupting audit continuity. Erasing a data subject deletes the key material needed to verify their linked sessions — the records remain structurally intact for the audit trail, but the content is permanently inaccessible and unverifiable.


Getting Started with AI Policy

If you’re building a new policy program with VeriProof:

  1. Choose your framework — Start with the regulation most urgent to your organisation (EU AI Act, NIST AI RMF, or SOC 2 are good starting points)
  2. Integrate the SDK — Capture sessions from your AI pipelines (Getting Started)
  3. Configure policy scoring — Define the thresholds that matter for your use case (Policy Scoring guide)
  4. Set up alert rules — Get notified when production behaviour deviates from policy (Alert Rules guide)
  5. Generate your first evidence package — Validate that the output meets your auditors’ expectations before you need it (Evidence Export guide)

Framework Deep Dives


Next Steps

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