04 / VERIFY · AI model, data, and artifact trust
Verify what your AI is made of, before it reaches production.
Verify the integrity, provenance, safety, and ownership of models, datasets, artifacts, and dependencies before and after release.
AI Supply Chain Security
Illustrative product view
RELEASE CANDIDATE / MODEL-V4-QUANTIZED
AI-BOM with source and evidence context
ComponentTypeEvidence sourceState
COMPLETENESS BOUNDARY
This inventory reflects the connected registry, build manifest, data catalog, supplied metadata, and supported artifact inspection for this release.
AI SUPPLY CHAIN INTEGRITY
Verify the models, datasets, artifacts, dependencies, and lineage entering production.
Models and datasets arrive through opaque vendor, open-source, fine-tuning, merging, and conversion chains. Traditional software scanners cannot explain model lineage, backdoored behavior, poisoned data, or how a derived asset inherited risk.
Opaque origin
Teams may not know the source, license, training or fine-tuning chain, dependency set, or approvals behind an AI asset.
Hidden artifact behavior
Unsafe serialization, embedded code, tampering, backdoored weights, vulnerable loaders, or anomalous structures can survive ordinary review.
Compromised data and IP
Poisoned datasets, feedback manipulation, extraction attempts, membership inference, and unauthorized cloning threaten integrity and ownership.
VERSION-BOUND RELEASE ASSURANCE
Connect every release to its identity, composition, lineage, and approval.
Supply-chain assurance joins exact artifact identity to inspection, inherited risk, accountable approval, and the release workflow.
SUPPLY CHAINRelease evidenceVersion-bound decision
Identity
Artifact, version, source, owner, intended use, repository, environment, observed hash, and approval state
Composition
Models, datasets, prompts, frameworks, dependencies, licenses, serving components, and supplied metadata
Integrity
Genealogy, provenance, signatures, parsed structures, serialization, code, weights, and supported investigation evidence
Release decision
Finding inheritance, limitations, required conditions, named approvals, exact version, and attestation state
OUTCOMES
Know what the artifact is, where it came from, what changed, and whether it may proceed.
Every output remains connected to the release candidate, upstream evidence, inspection scope, inherited findings, owner, and decision.
A verifiable AI-BOM
Inventory models, datasets, prompts, frameworks, dependencies, licenses, serving components, versions, and relationships.
Deep asset inspection
Inspect supported model and artifact formats for malicious code, unsafe serialization, tampering, vulnerabilities, and anomalies.
Lineage-aware risk
Follow risk across base, fine-tuned, merged, distilled, and quantized variants instead of treating each file in isolation.
Release evidence
Bind provenance, scan results, approvals, limitations, and signatures to the asset and release decision.
HOW SUPPLY CHAIN SECURITY WORKS
Inventory. Inspect. Detect. Protect. Attest.
The workflow follows the artifact from declared source and composition through inspection, decision, and version-bound evidence.
- 01Inventory
Build the AI bill of materials
Catalog models, datasets, prompts, frameworks, dependencies, licenses, serving components, versions, and owners.
- 02Inspect
Look inside the artifact
Parse supported formats for unsafe serialization, malicious content, structural anomalies, tampering, and vulnerable dependencies.
- 03Detect
Find compromised learning paths
Evaluate evidence of poisoned datasets, backdoored weights, anomalous behavior, and manipulated feedback loops.
- 04Protect
Defend model ownership
Surface extraction, membership inference, cloning, and other misuse patterns affecting proprietary model IP.
- 05Attest
Carry trust into release
Sign provenance, lineage, scan results, approvals, limitations, and release evidence for downstream verification.
RELEASE ATTESTATION
Bind the decision to the exact artifact and evidence set.
An attestation should explain why this version may proceed, which limitations remain, who approved it, and which change invalidates the decision.
- 01
Exact artifact identity
The release candidate, version, observed hash or signature state, source, owner, environment, and intended deployment.
- 02
Composition and genealogy
The available AI-BOM, upstream sources, transformation history, dependencies, datasets, licenses, and inherited findings.
- 03
Inspection and limitations
The scanner and rule versions, supported parser scope, observed evidence, unresolved questions, and analysis boundaries.
- 04
Decision and invalidation rule
The approval, rejection, quarantine, or condition; accountable reviewers; required action; and changes that invalidate the record.
PRODUCT CAPABILITIES
Five capabilities working as one AI Supply Chain Security product.
AI-BOM Registry, ArtifactGuard, PoisonScan, ModelTheft Defense, and release attestation support one release-assurance outcome.
AI-BOM & Genealogy
Component inventory and lineage across sources, versions, base models, fine-tunes, merges, distillations, and deployments.
Artifact Scanning
Format-aware inspection for malicious code, unsafe serialization, tampering, vulnerabilities, dependencies, and anomalies.
Poison & Backdoor Detection
Evidence and behavioral checks for compromised datasets, weights, fine-tuning, evaluation, and feedback loops.
Model IP Protection
Signals for extraction, membership inference, cloning, misuse, and unauthorized access to proprietary models.
Attestation
Verifiable provenance, results, approvals, limitations, signatures, and release evidence bound to the asset.
TECHNICAL DECISION SYSTEM
From exact artifact identity to a consumed release decision.
Supply Chain Security is the release-trust system. It must bind composition, genealogy, inspection, inherited risk, limitations, and approval to one exact artifact version.
PRODUCT ROLEAI-BOM, genealogy, artifact and data investigation, inherited risk, model-IP signals, and version-bound attestation.
Available product · deployment scopedWHERE THIS PRODUCT CHANGES THE DECISION
Move from product mechanism to the operating outcome.
A product may lead one solution and provide context or evidence in another. Its role is stated explicitly on each path.01 / GOVERNEvidence
Govern AI Adoption
Govern AI adoption with current evidence, accountable ownership, and risk-based approval.
Explore the solution →02 / CONTROLEvidence
Secure Agentic Operations
Authorize every consequential agent action with identity, purpose, context, and policy.
Explore the solution →03 / ASSURECo-lead
Assure AI Releases
Release AI systems with version-bound attack, control, and artifact evidence.
Explore the solution →DEPLOYMENT SCOPE
Confirm coverage for the selected artifact and release workflow.
Formats, repositories, loaders, inspection depth, genealogy sources, and release actions are confirmed before implementation.
Format support defines inspection
Artifact types, framework versions, serialization methods, repositories, registries, parsers, and analysis depth must be confirmed for the proposed scope.
An AI-BOM can be incomplete
Completeness depends on connected sources, supplied metadata, build evidence, supported parsers, and the components observable in the selected environment.
Provenance requires verification
A recorded origin or lineage statement does not prove every upstream assertion unless its source, signature, transformation, and evidence are independently verified.
Integrity is not safety
A matching hash or valid signature can show that an artifact matches an expected value; it cannot establish that the expected artifact is free from risk.
Absence cannot be universally proven
Poison and backdoor analysis depends on the technique, model, data, access, evaluation design, evidence quality, and supported inspection method.
Static inspection is one layer
Artifact analysis does not replace behavioral security testing, release engineering controls, monitoring, or runtime enforcement.
IP signals need defined telemetry
Extraction or theft indicators require observable evidence and do not alone establish attribution, ownership, intent, or legal infringement.
Attestation is version-bound
The record applies to an exact artifact and evidence set. A material model, dataset, dependency, configuration, or package change requires a new decision.
TECHNICAL FAQ
Formats, workflows, and operating fit.
Is this traditional software composition analysis?
It includes dependency and component context but extends the trust decision to AI-specific assets: model formats, weights, datasets, genealogy, poisoning, backdoors, provenance, and model-IP risk.
What is included in the AI-BOM?
The target structure includes models, datasets, prompts, frameworks, dependencies, licenses, serving components, versions, lineage, owners, scan evidence, approvals, and deployment relationships.
Can it scan every model format?
No product can safely imply universal format coverage. Supported formats, frameworks, repositories, loaders, and analysis depth are confirmed during technical review for each deployment.
How does genealogy improve security?
Genealogy helps a team understand which derived models inherit a vulnerable dependency, suspicious behavior, poisoned source, licensing issue, or expired approval from an upstream asset.
How is trust evidence used in production?
The platform carries provenance, inspection, approval, and lineage context into posture and runtime decisions so a deployed asset does not lose its history at release.
START WITH ONE RELEASE CANDIDATE