See the system. Trace what can act. Validate what can happen.
AI Security Workbench connects architecture, code, agents, tools, identities, permissions, runtime behavior, findings, controls, and evidence. Use that connected context to test realistic flows, validate consequential paths, identify remediation chokepoints, and preserve proof for engineering, security review, and partner workflows.
For AI product teams, security leaders, scanner providers, offensive platforms, and partners building security into existing products.
What you're facing
Traditional Security Testing Misses the AI Product
Traditional review misses model, retrieval, and authority risk.
Existing Scanners Stop at the Finding
AI code, retrieval, and agent context sit outside the finding.
Agents Can Exceed Their Authority
Tools, data, and approvals create unsafe blast radius.
AI Security Has No Baseline
Ownership, controls, and evidence are fragmented.
Your Platform Needs AI Security
Building it internally takes too long.
Research + Workbench
Evidence-backed engineering, not generic consulting.
Our engineering loop
One engineering loop from connected system context to defensible evidence.
- Architecture and trust boundaries
- Agents, tools, identities, and data flows
- Adversarial scenarios and runtime traces
- Code-risk, authority, abuse, and attack paths
- Controls, permissions, approvals, and boundaries
- Remediation chokepoints and retest conditions
- Provenance, claim state, remediation, and retest
- Engineering, partner, buyer, and assurance outputs
Supported outputs
Findings should not die in a PDF.
Turn connected security work into the artifacts teams already use: engineering tickets, structured findings, CI/CD outputs, remediation records, retest evidence, partner projections, buyer-ready summaries, and reviewed assurance material.
A finding should retain its identity, source evidence, relationships, claim state, remediation, and retest history as it moves from engineering analysis into security decisions and external review.
Jira
GitHub
GitHub Actions
Azure DevOps
Slack- Microsoft Teams
- SEServiceNow
Google Docs
Notion
Salesforce
HubSpot- EBEvidence bundle
- BUBurp Suite
- OWOWASP ZAP
Moodle- SCSCORM
- CLCLI / headless
- WHWebhooks
- JSJSON
- SRSARIF
- MDMarkdown
- PDPDF
- RTRetest checklist
- BEBuyer evidence
Expert-led security engineering. Workbench-backed analysis.
AI Security LLC leads assessments, adversarial testing, hardening, and evidence work using the AI Security Workbench. The Workbench connects system models, findings, workflows, authority relationships, runtime observations, control changes, and evidence without pretending that every signal is already a validated attack path.
Selected capabilities are also available through OEM and partner delivery. Expert-led services and software or embedded licensing remain distinct commercial options.
Built for the connected systems teams are shipping now
AI application risk spans code, prompts, retrieval, identities, tools, permissions, runtime behavior, model decisions, and the relationships between them. The Workbench analyzes those relationships instead of treating the model or finding as an isolated endpoint.
Agent and tool authority
MCP servers, callable tools, identities, credentials, scopes, approvals, browser actions, external effects, and dangerous capability composition.
Retrieval and data flows
Queries, authorization, corpus boundaries, provenance, context assembly, tenant isolation, poisoning, leakage, and output handling.
AI application code
Prompt construction, model invocation, agent orchestration, tool calls, source-to-sink relationships, unsafe actions, and code-risk paths.
Connected evidence
Findings, runtime traces, graph relationships, path state, remediation, retest conditions, analyst decisions, and structured partner or buyer outputs.
See the engineering behind the engagement.
Model the graph. Trace the flows. Validate the paths. Preserve the evidence.
Workbench proof
Where does AI code create a security-relevant path?
Code Scanner
Graph-backed AI code analysis for RAG, agents, MCP, browser automation, model integrations, and tool-calling applications. Correlate source, sink, data, tool, permission, and missing-control signals into reviewable code-risk paths, structured findings, validation plans, SARIF, and remediation evidence.
Explore Code ScannerWhere are the trust boundaries?
Threat Canvas
DFD-style AI threat modeling with Jira export and Confluence evidence.
Add graph-backed analysis without replacing your product.
The OEM Engine runs behind the partner workflow and returns structured findings, connected context, path state, evidence references, lifecycle status, remediation, and retest conditions while preserving the partner's original identity and product experience.
1{2"finding_id": "partner-98271",3"severity": "High",4"component": "agent/tool-runner",5"location": "services/agent/runner.py:142",6"description": "Retrieved context can influence a consequential tool call."7}
1{2"return_id": "workbench-demo-018",3"finding_id": "partner-98271",4"evidence_refs": ["source-path-142", "tool-policy-07"],5"code_risk_path": "retrieved_context -> agent_decision -> consequential_tool",6"path_state": "candidate_requires_validation",7"claim_state": "supported_code_finding",8"remediation": "Constrain tool arguments and require approval before execution.",9"lifecycle_state": "enriched_retest_required",10"validation_state": "not_validated_pending_retest"11}
Sanitized reference shape · not a customer result
Choose the path that fits the outcome you need.
Need us to deliver the outcome?
Fixed-scope mapping, assessment, adversarial testing, hardening, or evidence work using the AI Security Workbench.
Need the capability inside your product?
Embed selected analysis, contracts, projections, and evidence workflows through an OEM or partner integration.
One finding. Connected context for every audience.
The underlying finding keeps the same identity and evidence. Engineering receives the technical trace and remediation context. Security receives relationship and path analysis with explicit claim state. Buyers receive only reviewed, scoped evidence.
Returns through issue systems, CI/CD, structured APIs, evidence packs, and buyer-facing deliverables.
Built around the workflow you already own.
AI Product Teams
Secure agents, tools, and data before launch.
Security Product Platforms
Embed AI-native findings, connected context, path analysis, and evidence lifecycle support while keeping your engine, interface, workflow, and brand.
Workforce & Training Platforms
Add AI security role, skill, and readiness capability while keeping your learner experience.
Start here
Tell us what is shipping and what is blocked.
Share the system, the decision you need to make, and the timeline. We will recommend the smallest useful first step.
- Map the AI application: components, models, agents, tools, identities, permissions, data, and intended flows
- Test realistic failures and capture what actually happened
- Determine which findings and relationships form supported security paths
- Prioritize controls, remediation chokepoints, and retest conditions
- Produce engineering-ready work and reviewed evidence
Scope the first step, or browse the full engagement catalog.