03 / ASSURE · AI release assurance · red teaming · artifact trust
Release the evidence, not just the model.
Join exact artifact trust, realistic attack evidence, remediation, control verification, and limitations in one version-bound record for a release decision.
Bind every conclusion to the exact release, replay the demonstrated failure after the control changes, and record what would invalidate the result.
Attack, artifact, and release evidence
Release AI systems with version-bound attack, control, and artifact evidence.
Replace disconnected scan reports and red-team findings with a version-bound release record showing what was inspected, attacked, changed, replayed, and left unresolved.
BEFOREArtifact scans, red-team findings, remediation tickets, policy changes, and release approvals refer to different versions and cannot support one bounded conclusion.
→DECISION CHANGEVersion-bound AI release evidence packet
ILLUSTRATIVE SCENARIO MODEL
AI release assurance · red teaming · artifact trustHIGH-CONSEQUENCE SCENARIO
A release changes after the attack that justified its approval.
A release candidate includes a new model version, updated retrieval data, changed tool permission, and a policy intended to address a previously reproduced attack.
- Decision
- Replay against the exact candidate, confirm the permitted path, then approve, restrict, remediate, or defer with limitations.
- Consequence
- Without version identity and replay, a passing result can be attached to a system that was never actually tested.
RELEASE MANIFESTCandidate R-017
CandidateBOUNDModelBOUNDDataBOUNDToolsBOUNDPolicyBOUNDEnvironmentBOUND
- 01InspectArtifact and provenance
- 02AttackReproduced failure
- 03ChangeLinked control
- 04ReplayExact regression case
RELEASE GATEDEFER · REPLAY OPENExact replay remains open
LEGITIMATE PATH
The updated system completes the intended workflow using the reviewed artifact set, permissions, data, and policy.
RISK PATH
The prior attack is marked remediated, but the tested target and the release candidate no longer share the same model, data, tool, or control state.
Illustrative system and evidence model. It explains the decision structure; it is not a customer environment, product capture, compatibility statement, or measured result.
HOW IT WORKS
Move from system context to control and verification.
Follow the operating path from the initial scope through observation, action, and a verified outcome.
Illustrative solution workflow
Reference model · technology support verified for each scopeSelect a stage to inspect its input, decision, product role, and output. The reference model is published; named interfaces, deployment behavior, efficacy, and availability require representative evidence.
WHERE COSMIPHER FITS
Add connected AI decision context without replacing authoritative controls.
The reference architecture separates evidence sources, the Cosmipher decision boundary, the operating handoff, and the systems that retain authority.
REFERENCE ARCHITECTURE
TOPOLOGY AND SUPPORT VERIFIED FOR EACH SCOPEAuthoritative system context
Release manifest, model, data, prompts, code, dependencies, tools, policies, environment, provenance, and authorized test evidence
Connected decision evidence
Supply-chain inspection and security testing join artifact trust, reproduced failure, remediation, control, and exact replay
Decision consumed by the team
Version-bound release packet consumed by the release gate with limitations, residual conditions, owner, and invalidation triggers
This diagram shows the product relationship. Provider, interface, deployment mode, data path, and available control actions are confirmed for the selected environment.
SAST, SCA, and software supply chain
WHAT IT ALREADY SEES
Source code, packages, dependencies, build provenance, vulnerabilities, and conventional software artifacts.
COSMIPHER ADDS
Models, datasets, prompts, agent tools, AI-specific artifact formats, behavior under attack, and their connection to the release decision.
Model evaluation and quality testing
WHAT IT ALREADY SEES
Task performance, quality, robustness, safety, or benchmark behavior under the evaluation method used.
COSMIPHER ADDS
Security threat hypotheses, exploitable action paths, artifact trust, control linkage, exact replay, and bounded security conclusions.
CI/CD and release management
WHAT IT ALREADY SEES
Version, change, build, approval, deployment, rollback, and operating release state.
COSMIPHER ADDS
The version-bound artifact, attack, remediation, control, replay, limitation, and residual-risk evidence consumed by the gate.
OPERATING OUTCOMES
Pair the technical record with the work it makes possible.
These are intended operating consequences of the reference workflow, not measured performance or risk-reduction claims.
Exact release baseline
Tie the assessment to identifiable system components, versions, environment, configuration, and evidence sources.
Prevent an earlier test result from silently following a changed release.Artifact and attack evidence together
Connect provenance and inherited risk to the failure paths exercised against the assembled AI system.
Review composition and exploitable behavior in the same release decision.Verified control change
Link the confirmed mechanism to the remediation and replay the same attack against the changed release candidate.
Distinguish a closed ticket from a control that changed the observed result.Bounded release conclusion
State what the evidence supports, what remains unresolved, which conditions apply, and what change invalidates the decision.
Give the release owner an explicit approve, restrict, remediate, or defer record.START WITH ONE PRODUCT
Deploy the product closest to the immediate risk.
Add other Cosmipher products when the operating scope requires more posture, testing, runtime, or artifact context.
Cosmipher AI Security Testing
Maps the test boundary, exercises realistic attacks, preserves reproducible findings, guides remediation, and replays the exact case.
Inspect AI Security Testing →ADD ONLY WHEN THE EVIDENCE QUESTION REQUIRES IT
Cosmipher AI Supply Chain Security
ConditionalWhen: The decision requires exact artifact composition, provenance, integrity, lineage, or inherited-risk evidence.
Receives: Version-bound AI-BOM, provenance, and artifact findings.
Inspect AI Supply Chain Security →Cosmipher AI Firewall
ConditionalWhen: A confirmed failure is addressed through a runtime policy or control that must be replayed.
Receives: Policy decision, enforcement record, and control-verification context.
Inspect AI Firewall →AgentSPM
ConditionalWhen: Ownership, environment, reachability, lifecycle, or later change must remain connected to the release.
Receives: System context, accountable owner, approval state, and change trigger.
Inspect AgentSPM →REPRESENTATIVE OUTPUT
See how the result is structured and handed to the operating team.
The example uses non-customer data to show the information structure. It is not a live customer environment or a measured outcome.
ILLUSTRATIVE OUTPUT STRUCTURE
NOT A PRODUCT CAPTURE OR CUSTOMER RECORDDECISION ARTIFACT
Version-bound AI release evidence packet
Illustrative structure for the record joining the exact candidate, artifact findings, attack evidence, remediation, replay, and permitted release conclusion.
CURRENT DECISIONDEFER · REPLAY OPEN
- 01Release baselineRecorded
- Candidate, model, data, prompt, tools, policy, environment, and evidence identifiers
- 02Artifact trustReview
- Composition recorded · provenance condition remains under review
- 03Attack evidenceRecorded
- Reproduced tool-abuse path linked to the affected candidate
- 04RemediationReview
- Policy changed after the reproduced failure
- 05Release conditionOpen
- Exact attack replay and legitimate-path check remain open
COMPLETE EVALUATION DELIVERABLES
Every item remains connected to its source, scope, owner, version, limitations, and permitted conclusion.
Version-bound release baseline
The exact target, components, environment, evidence sources, exclusions, and change identifiers reviewed.
AI-BOM and provenance findings
Composition, lineage, integrity, inherited-risk observations, uncertainty, and required follow-up.
Reproducible attack record
Threat hypothesis, steps, trace, affected assets, consequence, severity rationale, and evidence needed to repeat the result.
Remediation and replay
The proposed change, linked control, exact regression case, observed result, and legitimate-path check.
Release decision packet
Approve, restrict, remediate, or defer, with scope, versions, evidence, owner, limitations, residual conditions, and invalidation triggers.
SCOPED EVALUATION
Validate the solution against one consequential system.
Scope, authorization, environment, information handling, stop conditions, and permitted activity are agreed before evaluation.
- 01Baseline
Freeze the decision target
Agree the release candidate, component and environment identifiers, authorized interfaces, legitimate behavior, exclusions, and stop conditions.
OUTPUTVersion-bound evaluation manifest - 02Inspect
Build artifact and attack evidence
Inspect the agreed artifact scope and exercise threat hypotheses selected for the system, authority, data, tools, and consequence.
OUTPUTArtifact findings and reproducible attacks - 03Remediate
Connect failure to the change
Identify the policy, permission, prompt, configuration, dependency, code, or architecture change that addresses the observed mechanism.
OUTPUTFinding-to-control linkage - 04Replay
Issue the bounded decision
Run the exact cases against the changed candidate, confirm the permitted behavior, and record limitations and invalidation conditions.
OUTPUTAI release evidence packet
EVALUATION EXITVersion-bound AI release evidence packet
The record states the boundary, evidence, result, owner, limitations, residual conditions, and the change that invalidates or reopens the conclusion.
Discuss this evaluation →DEPLOYMENT VALIDATION
Confirm technical fit for the selected environment.
Review the interfaces, deployment behavior, supported actions, and proof required before implementation.
Release evidence model
The baseline-to-replay and bounded-decision sequence shown on this page is the public reference workflow.
Artifact, test, and CI/CD support
Formats, methods, providers, execution environments, triggers, and gate behavior must be confirmed for the proposed release.
Coverage and control result
Attack coverage, detection confidence, reproducibility, control efficacy, and release impact require representative evidence.
A scan is not a complete release decision
Artifact inspection addresses the supported artifact and analysis scope. It does not by itself establish application behavior, runtime safety, provenance completeness, or compliance.
A test result is version-bound
The conclusion applies to the exact target, configuration, model, data, tools, controls, environment, and test conditions recorded in the evaluation.
Detection has uncertainty
Poisoning, backdoor, attribution, similarity, malicious-object, vulnerability, and behavioral findings require method-specific confidence and limitation statements.
Release evidence is not certification
The packet can support an internal release or risk decision but does not represent regulatory approval, certification, or a guarantee of future behavior.
TECHNICAL FAQ
Clarify scope, deployment, and operating fit.
Is this the same as AI red teaming?
Red teaming is one co-leading evidence source. The solution also binds the result to exact artifacts and versions, connects remediation to a control, replays the attack, and records the bounded release decision.
Is an AI-BOM sufficient to approve a release?
No. An AI-BOM supports composition and traceability. The release decision can also require provenance, integrity, inherited-risk, attack, control, operational, and governance evidence.
Does passing the tested cases prove the system is secure?
No. It supports a bounded conclusion for the recorded target, cases, methods, environment, controls, and time. Untested paths, future changes, adaptive threats, and method limitations remain explicit.
Can this operate as a CI/CD gate?
Yes, when the selected release workflow exposes supported triggers and control points. Interfaces, artifact formats, execution environment, failure handling, and gate semantics are confirmed before implementation.
What is the smallest useful evaluation?
One identifiable release candidate or high-consequence boundary, one legitimate path, a small set of relevant threat hypotheses, exact artifacts where available, and a decision owner.
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