One operating model to discover what AI exists, understand how it is built and connected, validate how it can be attacked, trace exploit paths across the environment, map governance requirements, and stop unsafe actions at runtime — across browser and desktop AI, IDE and CLI, repositories, CI/CD, AWS and Azure AI Foundry, MCP servers and enterprise data.
Bounded scope, synthetic data, measurable exit criteria
01
Inventory workforce AI, custom agents, models, tools and owners, separating approved workflows from unmanaged use, then baseline what each one is built from — frameworks, SDKs, MCP servers, prompts, dependencies and secrets.
02
Red-team the estate against direct and indirect prompt injection, unsafe tool use, data exfiltration, privilege abuse, poisoned context and destructive actions, rather than assuming the guardrails hold.
03
Connect Git to CI/CD, registries, cloud identities, agent runtimes, MCP tools, SaaS and data, then rank the toxic combinations by exploitability, reachability, privilege and blast radius.
04
Start in monitor mode, then hold the risky action and evaluate it against policy — redact, require approval, reroute, downgrade or deny — and prove no data left and production did not change.
Eight Assurance Domains
Map agent frameworks, models, SDKs, MCP servers, prompt and tool schemas, dependencies, secrets, SBOMs and provenance. Find vulnerable packages, poisoned artifacts, unsafe defaults and dangerous capabilities.
Explore the domainInventory browser, desktop, IDE and CLI AI usage by user, team, tenant, model, business purpose, data touched and outbound destination. Separate approved workflows from unmanaged AI.
Explore the domainReview AWS and Azure AI Foundry identities, IAM, secrets, network paths, model endpoints, logs, storage, deployment permissions, sandboxes and production access.
Explore the domainTest direct and indirect prompt injection, sensitive disclosure, jailbreaks, tool misuse, excessive agency, authorization bypass, unsafe autonomy, destructive operations and control bypass.
Explore the domainConnect Git to CI/CD, registries, cloud identities, agent runtimes, MCP tools, SaaS and data. Rank toxic combinations by exploitability, reachability, privilege and blast radius.
Secure connectors, vector stores, embeddings, ACL mapping, tenant boundaries, retrieval pipelines, data classification, poisoning resistance, lineage, model routing and sensitive-data egress.
Build the AI inventory, risk register, policies, approvals, control mappings, ownership, exceptions and evidence, aligned to ISO/IEC 42001, ISO/IEC 27001, NIST AI RMF and OWASP GenAI guidance.
Explore the domainMonitor browser, desktop, coding-agent, MCP and cloud actions. Before impact, hold the action and evaluate actor, resource, destination, arguments, privilege, business intent and policy — then prove the outcome.
Explore the domainWhat The Assessment Produces
Mapped
An AI asset and dependency inventory, a shadow-AI map of who uses what with which data, and an attack-path graph from entry point to production impact.
Ranked
A prioritized backlog of toxic combinations, control gaps and quick wins, scored so a medium on a complete path outranks a critical with no route to impact.
Proven
A 30/60/90-day roadmap with named owners, a governance and evidence pack, and proof on coverage, latency, bypass, non-release and non-execution.
Your SAST, DLP, CSPM and AI gateway each see a fragment. A 60-minute design workshop maps what AI is present, what it can reach, and whether the harmful action can be stopped before data leaves.