RINP // CYBERSECURITY SERVICES
AI Security · AIS-2 · Model Supply-Chain
From the model's source to the release pipeline, we assess supply-chain trust with evidence.
We examine the model, the data source, the dependencies, and the deployment pipeline. We verify findings across source provenance, integrity, license visibility, and access control. We deliver the bill of materials, the executive summary, the technical report, and the remediation plan together.
- The source provenance and bill of materials for the model, data, and dependencies are assessed together.
- Integrity and authorization controls across the build, the registry, and the deployment pipeline are examined.
- A shareable technical security package is prepared for audits and customer security reviews.
4 links
- 01
Source provenance
Artefact integrity + license
- 02
BOM & dependency
Data lineage + dependency hygiene
- 03
Build · CI · attestation
Signing and verification chain
- 04
Registry & deployment
Promote/rollback, audit log
Data → registry → evidence pack
What this service is, and is not
- It assesses source provenance and integrity controls across the model artifact, the dataset, the dependencies, and the deployment pipeline.
- It reports signing and attestation gaps together with an owner, the business impact, and a remediation priority.
- It builds a single supply-chain risk picture shared across leadership, security, and platform teams.
- Not the Generative AI Red Team; the runtime chain on the prompt, tool, RAG and MCP surface opens a different decision moment.
- Not AI for Pentest & Red Team; accelerating the internal offensive-delivery workflow with AI is a separate decision.
- Not a legal license opinion, accuracy/fairness validation, from-scratch foundation-model training advisory or destructive production validation; it runs within written authority, controlled access, white/gray-box maturity and an evidence-mode choice.
Scope and boundaries
- Model artefact integrity: weights, configuration, tokenizer and template packages
- Dataset lineage and source visibility
- Code and dependency: repo, lockfile and version pinning, SCA and secret-scan outputs
- Build and CI: authority boundaries, signing, attestation and artefact-production chain
- Registry: access control, promote/rollback, audit log and abuse scenarios
- Deployment topology: single environment, staging and production, multi-tenant, multi-region or shared platform
- Evidence producibility and audit trails: BOM/AI-ML-BOM, source-provenance gap analysis, attestation notes
- Hardening work list and evidence pack: decision support for management + a fix-first list for the technical team
- Prompt, tool, RAG, MCP and agent-behavior testing within the Generative AI Red Team scope
- Internal offensive-workflow design and enablement within AI for Pentest & Red Team
- Legal license opinion and contract interpretation
- Model performance, accuracy or fairness scientific validation
- From-scratch foundation-model training advisory or heavy GPU/cluster architecture
- DoS, stress or destructive tests and intrusive actions without written approval
- Unauthorized third-party assets and uncontrolled data exfiltration
- Written authorization, rules of engagement (RoE), stop criteria and rollback coordination are approved.
- Standard delivery requires white-box or gray-box access; a black-box-only expectation is a limited evidence-based pre-assessment and triggers a manual-scope flag.
- The model-pipeline list, pipeline and registry summary, deployment topology, existing signing and verification practices and, where possible, audit-log and BOM outputs are shared.
- Customer data is not exfiltrated; sensitive samples are processed on a minimum-data principle; the default retention is 30 days or adjusted by contract.
- The starting price is an approximate budget; the final proposal narrows once artefact maturity, access model and evidence expectation are clear.
- Model-pipeline list and definition: family, variants, version path, target environment
- Pipeline, registry and deployment summary; environment and test window
- Access details (white/gray/black-box) and existing signing, verification and policy practices
- Where possible, audit log, BOM outputs, MLOps and CI/CD documentation; emergency and escalation contacts
- Optional: control/audit-mapping expectation, reporting language (TR / TR+EN), regulatory framework, previous AI risk assessment reports
The engagement is conducted under written authorization, restricted access, an approved model inventory, stop conditions, and data-protection rules.
How we work: six steps from inventory to the technical security package
Kick-off & scope validation
Written authorization, rules of engagement (RoE), stop criteria, rollback coordination, the model-pipeline list, pipeline and registry summary, deployment topology, access level and evidence-mode expectation are defined; the starting view is drawn.
Inventory & evidence goals
The model-artefact inventory, dataset lineage, dependency and build pipeline, registry access and audit-log status, and deployment paths are read; evidence goals (BOM coverage, source-provenance pack, attestation coverage) are fixed.
Artefact, data, dependency, pipeline & registry checks
We examine the integrity and access controls across the model artifact, the data source, the dependencies, the build process, the registry, and the deployment paths.
Risk pre-assessment
We prioritize source provenance, license, dependency, registry authorization, and audit-log findings according to their business impact.
Source provenance, attestation & fix-first list production
We prepare the bill of materials, the source-provenance appendix, signing and attestation recommendations, and a remediation plan with clear owners.
Executive and technical delivery
We deliver the executive summary, the technical report, and a shareable technical security package. Once the customer has applied the controls, we plan a post-remediation retest at the appropriate scope.
What we deliver
Executive summary
- Supply-chain risk picture: summarizes the source-provenance, license, integrity, and access findings.
- Release summary: shows the controls to apply first and the residual risk.
- Review records: presents outputs that can be shared for audits and customer security reviews.
Technical report and supply-chain records
- Bill of materials: presents the model, data, code, and dependency components together with their sources.
- Technical report: explains the integrity, license, registry, and deployment-pipeline findings.
- Attestation assessment: highlights the gaps in the signing and verification chain.
- Remediation plan: ranks the findings by business impact and names the responsible owners.
Optional outputs
- Fix-Validation Sprint: a separate line for revalidating signing, policy, access or source-provenance fixes.
- Audit-ready control-mapping addendum: a defensible evidence order against customer review, due diligence and regulation.
- Safe-execution-boundary workshop, bilingual reporting or monthly evidence validation: priced as a separate line when needed.
Leadership sees the controls required to release the model. Platform and security teams track the source-provenance, integrity, and access findings.
Decision profile
- Duration
- 2–3 weeks (by scope and package)
- Rhythm
- Delivery by evidence-mode choice
- Delivery
- Management + technical
- Scope
- Model pipeline, data, dependency, pipeline, registry
- Best for
- AI Platform teams running a model pipeline
Which is the right start, and when?
These three approaches do not produce the same evidence object. Starting without knowing the difference wastes time and budget chasing the wrong proof.
| Criterion | ÖnerilenAI Model Supply-Chain Assurance | Generative AI Red Team | General model-usage review |
|---|---|---|---|
| Decision question | What do we truly trust about the model and the release pipeline? | At runtime, which chain truly breaks, and which control cuts it? | Where does our model usage stand against a general checklist? |
| Primary evidence object | Source provenance, component list (BOM), attestation, registry and release trust, evidence pack | Broken runtime chain, abuse path, agent behavior, chain-breaking control | Checklist result, posture score, generic model-usage map |
| Ideal trigger | A new model version; onboarding a vendor or open-source model; an evidence-pack need for customer review or audit | Go-live of a GenAI product; adding a new tool, connector or MCP; agent authority rising to write/execute | A general AI risk-awareness round; a panoramic summary for senior management |
| Wrong match | Meeting a runtime-chain need with model supply-chain assurance | Meeting a model-trust-chain need with a runtime-chain test | Substituting a generic checklist result for a proven-trust-chain decision |
One example of decision clarity
Anon case
Before release: what do we truly trust about the model and its pipeline?
An organization gave us a scope to assess the model artifact, the data source, and the deployment pipeline before taking a third-party model into production. Together we defined the access level, the expected technical records, and the boundaries of the review.
Our review surfaced priority findings in the source-provenance records, the dependency inventory, and the registry authorization. We verified the findings with the bill of materials and the technical records.
We delivered the executive summary, the technical report, and the remediation plan together with a shareable technical security package. After the customer applied the signing and authorization controls, our retest confirmed that the priority findings had been remediated.
What this case produced
- The release decision was tied to a proven model trust chain, not assumption.
- Source provenance and license gaps were made visible by a BOM.
- The bill of materials and technical security records were made shareable with the audit team.
Let's clarify your model-pipeline scope together
This form helps us clarify the model, the data sources, the dependencies, the build process, the registry, the deployment pipeline, and the access level.
Frequently asked questions
Model Supply Chain Assurance examines the model's source, its components, and its deployment pipeline. GenAI Red Team, on the other hand, tests the product's runtime flow and agent behavior.
This service assesses the model and the deployment pipeline. To improve an in-house offensive team's AI workflow, the AI for Penetration Testing and Red Team service is used instead.
Legal license opinions and evaluations of model accuracy or fairness are out of scope. License and source-provenance findings are reported as technical risks.
Black-box access yields limited evidence. Producing the bill of materials and source provenance requires access to the repository, the build, and the registry.
The list of models and data sources, the dependencies, the build process, the registry, the deployment topology, and the access roles should be shared before the engagement.
Read-only access, least privilege, logging, and approval gates are used. Models and data are not shared outside the engagement scope.
Related services and add-ons
If your need extends beyond AI Model Supply-Chain Assurance, open the right bridge here.
AI for Pentest & Red Team
If the focus is controlled acceleration of the internal offensive-security team's discovery, evidence, reporting and retest workflow with human-supervised AI, AI for Pentest & Red Team is the right start.
PT-3Cloud Security Penetration Test
If the focus is the privilege chain, data-access path and transitive-authority visibility on the underlying cloud surface of the model registry, build/CI and deployment pipeline, the Cloud Security Penetration Test is the right start.
RTModular Red Team Simulation
If the focus is supply-chain exploitation within an enterprise attack path, crown-jewel impact and the detection gap, the Modular Red Team Simulation is the right start.
// AIS-2 · DISCOVERY
Let's clarify your model inventory, the expected technical records, and the review scope together.
In a discovery call we define the model source, the data and dependency chain, the build and registry structure, and the expected technical security package together.