Continuous Attestation
The Evidence Layer for AI
Evidence that your AI controls actually executed. Every prompt, response, and policy decision gets cryptographically signed and third-party witnessed — without sensitive data leaving your environment.
Zero Egress*
Sidecar mode
Non-blocking witness
Read-only observer
Tamper-proof
Crypto signatures
~5ms
Zero slowdown
Zero-Egress Attestation
You don’t need a third party to see sensitive data to prove integrity. Receipts are generated locally — hashes and signatures, not payloads — then anchored to an independent witness network. Like notarizing a document without the notary reading it.
S3 Object Lock / WORM Storage
Proves your logs weren’t modified after storage. Doesn’t prove the de-identification actually executed before data hit the model.
Continuous Attestation
Cryptographic proof generated at the moment of execution. We attest that controls ran before data reached the model — not just that logs exist.
How It Works
Every time your AI processes a request, GLACIS generates cryptographic proof that your controls executed.
Your AI Acts
Your AI processes a request. Safety controls execute: content filtering, bias checks, PII detection, guardrails. GLACIS observes which controls ran and their outcomes — without touching your data.
We Witness
GLACIS creates a cryptographic attestation: a signed, timestamped record proving exactly which controls executed, in what order, with what parameters. This happens in ~5ms — you won’t notice it.
Evidence Is Sealed
Attestations are stored with cryptographic chaining. Any attempt to modify, delete, or reorder records is cryptographically detectable. The evidence integrity is mathematically provable.
You Get Proof
Auditors, customers, or regulators can independently verify any attestation. No trust required in GLACIS or your organization. The math proves it.
Why This Matters
Traditional Approach
- Annual audits sample a fraction of interactions
- Policies say what should happen
- Logs can be altered after the fact
- Months between control check and evidence
Continuous Attestation
- Every AI interaction generates proof
- Attestations prove what actually happened
- Cryptographic signatures prevent tampering
- Evidence generated at time of execution
What You Can Prove
Safety Controls
Content filtering, harmful output detection, and guardrails executed on every inference.
Bias Testing
Fairness checks ran on model outputs with verifiable test parameters and results.
Data Privacy
PII detection, data masking, and access controls applied before data reaches the model.
Audit Trails
Complete, immutable record of who accessed what, when, and what the AI did with it.
Model Versioning
Proof of exactly which model version processed each request. No confusion about what ran.
Response Times
Latency and performance metrics with cryptographic timestamps. SLA compliance evidence.
Mapped to Frameworks You Need
Attestations automatically map to the compliance frameworks your customers and regulators require.
Ready for Continuous Proof?
Start with an Evidence Pack Sprint to establish your baseline, then add Continuous Attestation for ongoing proof.
Related Resources
Evidence Pack Sprint
Board-ready compliance evidence in days, not months.
The Proof Gap
Why documentation isn’t enough for AI compliance.
NIST AI RMF Guide
Complete guide to implementing the AI Risk Management Framework.
What Is AI Attestation?
Cryptographic proof that your AI controls executed as designed.
AI Audit Guide
Preparing for third-party AI compliance audits.