What is AI Attestation?

What is AI Attestation?

AI attestation is the process of generating cryptographic, tamper-evident records that prove specific controls, guardrails, and compliance mechanisms executed correctly during AI system operations. Each attestation record is a cryptographically-signed "receipt" documenting exactly which controls ran, when they ran, what parameters were used, and what outcomes occurred.

The concept draws from established practices in software supply chain security, where attestation frameworks like SLSA (Supply-chain Levels for Software Artifacts) and in-toto provide cryptographic proof of build provenance. AI attestation extends these principles to runtime: rather than proving how software was built, it proves how AI systems behave when processing actual data.

The Core Promise

Traditional AI compliance relies on three pillars: policies (what should happen), configurations (what is set up), and logs (what was recorded). Each has a fundamental weakness:

• Policies describe intent but don’t prove execution. Your privacy policy says you de-identify data; that doesn’t mean de-identification actually happened.
• Configurations show controls are enabled but don’t prove they triggered. A guardrail can be configured yet never fire if the conditions aren’t met.
• Logs record events but can be modified after the fact. Without cryptographic integrity, logs prove nothing to a skeptical auditor.

AI attestation solves all three: it proves controls executed (not just that policies exist), captures execution at runtime (not just configuration), and uses cryptographic signatures that prevent tampering (unlike standard logs).

Formal Definition

An AI attestation is a cryptographically-signed, timestamped record asserting that a specific AI control or set of controls executed with defined parameters and produced defined outcomes during a specific AI system operation. The attestation:

• Is generated at the moment of control execution, not after
• Includes a cryptographic signature preventing modification
• Is chained to previous attestations, making insertion/deletion detectable
• Can be verified by third parties without trusting the attestation creator

The Verification Vacuum

Why does AI attestation matter now? Because AI compliance has a fundamental problem that traditional approaches cannot solve: the verification vacuum.

Consider a healthcare AI system processing patient data. The organization has:

• A BAA with the AI vendor
• Policies requiring PHI de-identification before LLM processing
• A de-identification module configured in the pipeline
• Logs showing the de-identification service was called

Now an auditor asks: "For the 50,000 patient interactions last month, can you prove PHI was de-identified before reaching the LLM for each one?"

The organization cannot answer with certainty. The logs show the de-identification service was called, but not that it succeeded for every field. The configuration shows de-identification is enabled, but a bug could have bypassed it. The policy says de-identification should happen, but policies are not proof.

This is the verification vacuum: the gap between what organizations claim their AI systems do and what they can actually prove.

Why Traditional Approaches Fail

The Stakes Are Rising

The verification vacuum becomes existential as regulations mature. EU AI Act Article 12 requires high-risk AI systems to have "automatic recording of events (’logs’)" enabling "the tracing back of the system’s operations" and verification of compliance "throughout the lifetime of the system." Logs that can be modified don’t satisfy this. Policies that aren’t proven don’t satisfy this.

Similarly, when enterprise customers send AI security questionnaires asking "How do you ensure PII is protected in AI processing?" they increasingly want evidence, not assurances. The answer "we have a policy" is no longer sufficient.

How AI Attestation Works

AI attestation systems operate as an observability layer for AI control execution. Here’s the technical flow:

Step 1: Control Execution Observation

When an AI system processes a request, various controls execute: content filters, PII detectors, guardrails, bias checks, consent verifiers. The attestation system observes each control’s execution, capturing:

• Control identifier: Which control executed
• Execution timestamp: Precise time of execution
• Input hash: Cryptographic fingerprint of input (not the input itself)
• Configuration state: Control parameters at execution time
• Outcome: Pass/fail/warning and any actions taken

Step 2: Attestation Record Creation

The observed data is assembled into an attestation record. This record is then:

• Timestamped with a cryptographic timestamp from a trusted time source
• Signed with a private key controlled by the attestation system
• Chained to the previous attestation record via hash linking

The chaining is critical: each attestation includes the hash of the previous attestation. This creates a tamper-evident chain where any modification, insertion, or deletion breaks the chain and is mathematically detectable.

Step 3: Storage and Verification

Attestation records are stored in tamper-evident storage. When verification is needed (audit, customer request, regulatory inquiry), the chain can be validated:

• Each signature can be verified against the public key
• Each chain link can be verified by recomputing hashes
• Timestamps can be verified against the time source

Critically, verification requires no trust in the organization that created the attestations. The math proves it.

Attestation vs. Logging vs. Monitoring

AI attestation is often confused with logging and monitoring. While related, they serve fundamentally different purposes.

Why Logs Aren’t Enough

Organizations often say, "We have comprehensive logging, isn’t that sufficient?" Consider what an auditor examining logs must accept on faith:

• The logs haven’t been modified since creation
• No log entries have been deleted
• No fake entries have been inserted
• The logging system didn’t miss any events
• The timestamps are accurate

With attestation, the auditor accepts none of this on faith. The cryptographic signatures and chain prove integrity mathematically.

Why S3 Object Lock Isn’t Enough

Some organizations use WORM (Write Once Read Many) storage like S3 Object Lock, believing this provides attestation-level guarantees. It doesn’t.

WORM storage proves logs weren’t modified after storage. It doesn’t prove:

• The logs were accurate when written
• Controls actually executed (vs. logging a lie)
• No events were omitted before writing

Attestation generates proof at the moment of execution. WORM storage protects what was written. The two are complementary but not equivalent.

What Controls You Can Prove

AI attestation can generate proof for any control that executes during AI system operation. Common categories include:

Regulatory Drivers

Multiple regulatory frameworks are converging on requirements that only attestation can satisfy. While not all use the term explicitly, they require the verifiable evidence that attestation provides.

EU AI Act Article 12: Logging and Traceability

Article 12 of the EU AI Act requires high-risk AI systems to have "automatic recording of events (’logs’)" that enable:

• Tracing of system operations throughout the lifecycle
• Monitoring of system operation with respect to risk
• Post-market monitoring and incident investigation

The regulation requires logs to be "as relevant as technically possible" for demonstrating conformity. Standard application logs fail this test because they cannot prove conformity with certainty. Attestation satisfies Article 12 by providing verifiable evidence of control execution.

Deadline: August 2, 2026 for high-risk AI systems.

Colorado AI Act: Documentation Requirements

Colorado’s AI Act (SB 21-169, effective February 1, 2026) requires deployers of high-risk AI systems to implement "a risk management policy and program" and maintain documentation of:

• How the AI system was used in the consequential decision
• Steps taken to ensure accuracy and prevent discrimination
• Human oversight measures applied

When consumers challenge AI-influenced decisions, organizations must demonstrate their controls worked for that specific interaction. Attestation provides this evidence.

HIPAA: Audit Controls for AI Processing PHI

HIPAA’s Security Rule (45 CFR 164.312(b)) requires "hardware, software, and/or procedural mechanisms that record and examine activity in information systems that contain or use electronic protected health information."

When AI systems process PHI, auditors increasingly expect evidence that:

• De-identification executed before data reached the model
• Access controls enforced for each interaction
• Minimum necessary principle applied

Traditional logs cannot prove de-identification executed successfully. Attestation can.

NIST AI RMF: Continuous Monitoring

The NIST AI Risk Management Framework recommends organizations "continuously monitor AI systems for changes that may impact trustworthiness" and maintain "documentation of AI system behavior."

The framework’s "Govern" function specifically calls for "mechanisms are in place to document decision-making processes" and "accountability structures." Attestation provides the technical mechanism to satisfy these governance requirements with verifiable evidence.

Implementation Approaches

Organizations implementing AI attestation typically choose between three approaches:

Approach 1: Embedded Attestation

The attestation system runs as a sidecar or embedded component alongside AI workloads, directly observing control execution. This provides the strongest guarantees because the attestation system sees actual execution, not reported execution.

Pros: Highest assurance, lowest latency, tamper-resistant by design

Cons: Requires infrastructure integration, deployment complexity

Approach 2: API-Based Attestation

Controls report their execution to an attestation API, which generates signed records. This is easier to integrate but relies on controls accurately reporting their state.

Pros: Easier integration, works with existing infrastructure

Cons: Controls could misreport, requires trust in reporting mechanisms

Approach 3: Log Enhancement

Existing logs are enhanced with cryptographic signatures and chaining after generation. This provides some tamper-evidence but cannot prove the original logs were accurate.

Pros: Minimal changes to existing systems

Cons: Weakest assurance, doesn’t prove control execution

Key Implementation Considerations

Frequently Asked Questions