Service · ASSESS
AI Product Security Assessment
Map the AI product as a connected system, test material failure and abuse flows, qualify consequential paths, and turn the result into remediation and evidence.
Decision answered
What are the material security paths, control gaps, and remediation priorities across this AI product?
Duration
Typical duration: 2–4 weeks, depending on scope.
Primary output
AI Product Security Assessment Report and prioritized remediation backlog
Best for
Teams that need a deeper product-security view than a bounded launch review.
Engagement type
Scoped assessment
What is in scope
- Architecture and system boundaries
- Application code and integrations
- Models, providers, prompts, and retrieval
- Agents, MCP, tools, identities, and permissions
- Data flows, runtime behavior, controls, and evidence
Inputs needed
- Authorized system and assessment boundary
- Architecture and data-flow materials
- Relevant code, configurations, and test access
- Control, incident, and prior finding context
What the work actually does
- System and trust-boundary map
- Reproducible high-impact findings
- Prioritized remediation backlog
- Control and evidence gaps
- Executive and engineering summaries
What the work delivers
- System Boundary Record
- Material Finding and Observation Set
- Prioritized remediation backlog
- Control and evidence gap summary
- Executive and engineering assessment report
Evidence produced
- Supported system and trust-boundary observations
- Reproduced behavior within the authorized scope
- Finding-to-remediation relationships
- Retest conditions for consequential findings
Boundary
What this engagement does not establish
- A certification or compliance determination
- A guarantee that the system has no vulnerabilities
- Testing outside the authorized scope
- An implication that every assessment uses every available technique
After the engagement
Prioritize remediation, assign owners, decide release or acceptance conditions, and schedule targeted retest or deeper adversarial work where justified.
Optional deliverables: Retest Record, Claim-Readiness Matrix. Selected according to engagement scope.
Supporting Workbench capabilities
Selected according to scope.
The engagement outcome and evidence are the deliverable. These AI Security Workbench capabilities support the work where they add value; their presence here does not mean every engagement uses all of them.
Application Surface Discovery
Identify relevant AI application, provider, integration, and runtime surfaces.
Threat Canvas
Model system boundaries, trust relationships, and material abuse hypotheses.
Code Scanner
Find code-derived observations and candidate validation paths where source is in scope.
Attack Path Analysis
Qualify connected paths only where the evidence supports them.
Evidence System
Preserve findings, decisions, remediation, and retest relationships.
Relevant research
Delivery / subject-matter leads
Adjacent services
Choose by the decision you need to make.
LAUNCH
AI Launch Security Review
What must be fixed, accepted, or evidenced before this AI feature ships?
BASELINE
Expert-Led AI Security Program Baseline
Where should the AI security program start, and which ownership, control, and evidence gaps need priority work?
SELL
AI Security Sales Enablement
What AI security claims can the company safely make, and what evidence can support buyer review?