Know What's Running.
Trust What's Deployed.
AI-Verify uses interpretability techniques to confirm every model in your environment is exactly the official, approved asset — not an altered, backdoored, or supply-chain-compromised version.
— What Is AI-Verify
Interpretability-powered model integrity,
from source to production.
Model provenance is the new perimeter. AI systems fail silently, not through crashes, but through substitution. A model downloaded from a registry, pulled from a vendor, or deployed from an internal pipeline may look identical to the approved version. Checksums pass. Performance benchmarks pass. But the model has been tampered with.
AI-Verify applies interpretability analysis, examining internal activations, weight distributions, and behavioral signatures, to determine whether a deployed model matches its approved baseline at a structural level. No behavioral test can catch this. Only looking inside the model can.
This is the discipline of AI supply chain security: treating every model as an artifact that must be verified, attested, and continuously monitored, not just at deployment, but in production.
— Why Traditional Validation Fails
Standard checks don't
see inside the model.
Hash verification only checks the file, not the model
A cryptographic hash confirms the bytes didn't change in transit. It says nothing about whether the model was tampered with before packaging or whether the "official" release was itself compromised upstream.
Benchmark evals measure performance, not provenance
A backdoored model passes accuracy tests with the same scores as the clean version. Backdoors are designed to be invisible during normal operation, they activate only under specific trigger conditions your eval suite will never encounter.
Output monitoring catches consequences, not causes
Runtime behavioral monitoring alerts you after a model has already misbehaved. AI-Verify catches tampered models before they produce a single harmful output.
Unauthorized fine-tuning leaves no audit trail
An insider or compromised vendor can fine-tune a model to introduce subtle biases or dormant behaviors. The modified model ships with the same name, version, and changelog as the approved artifact. Interpretability is the only way to detect the divergence.
— How AI-Verify Works
Three layers of verification,
one definitive answer.
Baseline
Establish the Approved Baseline
Profile the official model to create a cryptographically-anchored interpretability fingerprint.
Scan
Scan Deployed Models Against Baseline
Compare internal representations across all deployed models to detect structural divergence or substitution.
Attest
Attest, Alert, and Continuously Monitor
Issue signed attestations and trigger immediate alerts on any post-deployment deviation from baseline.
— Capabilities
Full model integrity coverage,
across every stage of your AI pipeline.
Interpretability Fingerprinting
Generates a structural fingerprint from internal model activations and weight-space geometry, a signature that changes measurably when a model is modified.
Supply Chain Attestation
Signs every verified model with a tamper-evident attestation record linking it to the approved baseline, build pipeline, and deployment context.
Tamper & Backdoor Detection
Detects unauthorized fine-tuning, weight poisoning, and implanted backdoors by identifying structural divergence invisible to behavioral testing.
Vendor & Registry Scanning
Scans models from third-party vendors, open-source registries, and internal model stores before they reach production, flagging any artifact that deviates from its approved specification.
Continuous Integrity Monitoring
Continuously re-checks running models to detect post-deployment modifications, live-patching attacks, or drift from the certified baseline over time.
Compliance & Governance Evidence
Every verification event generates machine-readable audit records aligned to NIST AI RMF, ISO 42001, EU AI Act, and internal governance frameworks.
— The Results
What teams using AI-Verify are achieving.
100%
Supply chain artifact coverage before production deployment
<2s
Average time to verify a model against its approved baseline
0
Tampered models passing AI-Verify attestation
"We had no way to know if the model we deployed was the model we approved. AI-Verify answered that question and closed a gap we didn't realize was open."
— Enterprise AI Security Team
— Where AI-Verify Applies
Built for every team responsible for AI trust.
Security & Red Teams
Verify third-party and open-source models haven't been poisoned before they reach your environment. Run interpretability scans as part of your AI security review process.
MLOps & Platform Engineering
Integrate AI-Verify into your CI/CD pipeline as a verification gate. No model ships to production without a signed attestation confirming it matches the approved artifact.
AI Governance & Compliance
Generate audit-ready evidence that models deployed in regulated workflows are certified, verified, and unmodified. Meet EU AI Act, NIST AI RMF, and ISO 42001 supply chain requirements.
Procurement & Vendor Risk
Before accepting a vendor-supplied or contracted model, run AI-Verify to confirm it matches the specification you evaluated and approved — not a substituted or quietly-modified version.
— COMMON QUESTIONS
Frequently asked questions.
How do we enroll a new model and establish its approved baseline?
What does a failed verification look like, and what's the remediation path?
A failed scan produces a detailed divergence report identifying where the candidate model's internal structure deviates from the approved baseline, activation patterns, weight-space geometry, or both. The model is blocked from receiving a signed attestation, which can be configured as a hard deployment gate in your CI/CD pipeline. The report gives your security and MLOps teams the information they need to investigate whether the deviation is the result of unauthorized modification, supply chain substitution, or a sanctioned change that simply wasn't re-enrolled.
How does AI-Verify handle models that are intentionally updated over time?
Sanctioned updates are treated as re-enrollment events. When your team approves a new version (whether a vendor release, a fine-tuned variant, or an internally retrained model) that version becomes the new approved baseline with a versioned attestation record. AI-Verify maintains a full history of enrolled baselines, so you have a complete lineage of every approved artifact ever deployed. Unauthorized modifications between enrollment events are what the system is designed to catch.
Which compliance and governance frameworks does AI-Verify's attestation evidence support?
Yes. AI-Verify is available as a self-hosted deployment for environments where model artifacts cannot leave the network perimeter, regulated industries, classified environments, or organizations with strict data residency requirements. The interpretability analysis runs entirely within your infrastructure. Attestation records are generated locally and can be signed with your own PKI. Contact the team to discuss deployment architecture for air-gapped environments.