01 / REQUEST
Request arrives
An AI request enters the GLACIS Enforce module. Enforce sits inline in your request path — every interaction passes through it before reaching your model or returning to the user.
Runtime Assurance Platform · Evidence receipt layer
Continuous attestation is the steady stream of signed evidence receipts emitted by local runtime controls inside your infrastructure. Receipts are continuously assembled into evidence packs your board, your customers, and your regulators can verify — with zero sensitive-data egress.
How it works
Every time your AI acts, local runtime controls inside your infrastructure emit a signed evidence receipt for the decision that ran.
01 / REQUEST
An AI request enters the GLACIS Enforce module. Enforce sits inline in your request path — every interaction passes through it before reaching your model or returning to the user.
02 / CONTROLS
Safety controls run: content filtering, bias checks, PII detection, consent verification. Each control’s outcome is recorded as it executes.
03 / POLICY
Enforce evaluates your active governance posture and renders a decision: PERMIT, DENY, escalate, or flag. The decision is applied inline — non-compliant requests are blocked before they reach the model.
04 / SEAL
A cryptographic attestation is generated — signed, timestamped, and chained. Any attempt to modify, delete, or reorder records is cryptographically detectable.
05 / VERIFY
Auditors, customers, or regulators can independently verify any attestation. No trust required in GLACIS or your organization. The math proves it.
06 / ASSEMBLE
Receipts continuously assemble into a board- and audit-ready evidence pack mapped to NIST AI RMF, ISO/IEC 42001, the EU AI Act Article 12 logging duty, and Colorado/Texas state requirements.
Deployment modes
Every mode change is itself attested.
Observe all traffic, evaluate against policy, generate receipts. Never block. Perfect for baselining your governance posture before enforcement.
Evaluate and alert on policy violations. Generate receipts with violation flags. Don’t block requests — let your team review before enabling enforcement.
Block policy violations with denial receipts. Permit compliant requests. Every decision — permit and deny — is independently attested.
Block violations and circuit-break when violation thresholds are exceeded. For environments where policy breaches require immediate pipeline shutdown.
Requests denied if Enforce is unavailable. Safety takes priority over availability. No request proceeds without governance evaluation.
Requests proceed with a flag if Enforce is unavailable. Availability takes priority. The unevaluated request is logged and flagged for retroactive review.
Why this matters
What you can prove
Content filtering, harmful output detection, and safety controls executed on every inference.
Fairness checks ran on model outputs with verifiable test parameters and results.
PII detection, data masking, and access controls applied before data reaches the model.
Complete, immutable record of who accessed what, when, and what the AI did with it.
Proof of exactly which model version processed each request. No confusion about what ran.
Latency and performance metrics with cryptographic timestamps. SLA compliance evidence.
Book a focused Agent Runtime Security & Evidence Sprint, then deploy runtime assurance where the risk is real.