AI security
We test the connections between models, data, identities, and tools. Find exploitable paths in AI-enabled systems, build the controls to limit them, and give defenders the evidence they need.
AI security services
Start with an architecture decision, a deployed workflow, or a control that needs to hold. Scope the work around what your AI can reach and what it is allowed to change.
Engagement 01
Where does the system place its trust?
Map the path from a user request to retrieved data, model reasoning, delegated identity, and action. Identify the boundaries an attacker could cross.
The handoff
A prioritized threat model with abuse paths, affected boundaries, and control recommendations.
Engagement 02
Can that trust be turned into an attack?
Test agreed scenarios across the application and its supporting systems. Follow the consequences of a manipulated prompt or tool call beyond the model response.
The handoff
Reproducible findings, attack evidence, and verification criteria for the controls that need to change.
Engagement 03
What should the system be allowed to do?
Design and validate controls around the actions an AI workflow can take. Enforce permissions in the application and runtime, not only in the prompt.
The handoff
A hardening plan with enforceable boundaries and tests to check their behavior.
Engagement 04
Can your team see and contain misuse?
Connect prompt, retrieval, and action telemetry to the investigation workflow. Define what to capture, when to escalate, and how to contain the affected capability.
The handoff
Telemetry requirements, response actions, and validation scenarios for the defending team.
Engineering the controls
A prompt is not a permission boundary. Controls need to hold at the points where the system reads data, uses an identity, executes code, or reaches another service.
Restrict code execution, network access, and tool behavior outside the model. Test what happens when a workflow tries to exceed those limits.
Separate reasoning from action privileges. Limit delegated credentials by purpose, scope, and lifetime.
Check artifact provenance, retrieval trust, and deployment integrity across model, data, and software supply chains.
Preserve the traces needed to connect a request to an action, investigate a violation, and contain affected tools or sessions.
From our research
A catalog of attack scenarios, control mappings, and verification references used in our research. These are baseline scenarios—not findings about your organization or a live threat feed.
Research snapshot: . Scores, suggested owners, and residual-risk labels reflect this baseline, not a customer assessment.
Search the catalog, then open a scenario for its baseline score and framework references.
38 matching scenarios · Showing 6 of 38
The snapshot contains 1,064 risk-control pairs, including direct and adjacent mappings. A mapping is a research relationship, not proof that a control has been implemented or that a risk has been eliminated. Framework references support assessment planning; they do not imply certification or endorsement.
What you receive
Deliverables follow the agreed scope. The aim is to make the risk understandable, the fix actionable, and the next test clear.
Before we begin
Tell us what the system does, where it runs, and the actions or data that matter most.
Scope an AI assessmentKeep credentials, sensitive prompts, and production data out of the initial web inquiry.
Active incident? Get response supportYes. The assessment can include the application, retrieval pipeline, tools, identities, runtime, and supporting infrastructure. We agree on the relevant components and authorized actions before testing.
Yes. We examine how an agent delegates work, accesses data, calls tools, and takes actions. For retrieval-augmented generation, testing can include source trust, access boundaries, and how retrieved content influences the workflow.
No. It is a dated snapshot of our research catalog, not a live assessment of your environment. Risk bands, scores, suggested owners, and residual-risk labels reflect that baseline. An engagement establishes the context and evidence for your system.
No. Testing provides evidence within an agreed scope and set of conditions. It cannot establish that every model behavior or future attack has been covered. Findings include assessment limits and remaining risks.
Yes. Control engineering and validation can be included in the engagement. Implementation responsibilities, access, retest scope, and acceptance criteria are agreed with your team.
Describe the workflow, the decisions or actions it supports, and the stage of development. Keep credentials, sensitive prompts, production data, and model artifacts out of the initial web inquiry; we can arrange an appropriate exchange after scoping.
