AI Exposure Derisk

How we integrate with your stack

We work with your existing investments in exposure management, assessment, and security analytics to govern AI adoption with evidence, not policy decks alone.

• Endpoint and cloud visibility

  • Correlate endpoint and identity signals with approved AI tooling scope
  • Support shadow-AI discovery conversations with operational telemetry
  • Align detection priorities with data-flow and access risks from new AI services

• Exposure analytics and prioritization

  • Prioritize AI-related infrastructure and integration exposure
  • Track remediation progress against measurable risk reduction
  • Feed executive reporting with exploitability-focused findings

• Continuous assessment and policy alignment

  • Map misconfigurations on systems supporting models and AI pipelines
  • Connect cloud and on-prem exposure to AI governance decisions
  • Maintain a single prioritized backlog across programs

Representative engagements

• Technology

  Approved AI stack and data-flow guardrails

  A SaaS provider accelerated Copilot and LLM adoption across engineering and GTM teams. We mapped sensitive data flows, defined approved tooling, and aligned technical controls with product delivery timelines.

  • Documented data boundaries for customer PII in AI workflows
  • Reduced shadow-AI sprawl with clear allow lists and monitoring hooks
  • Executive-ready risk register tied to remediation owners
• Professional services

  Shadow-AI discovery and governance uplift

  A distributed professional services firm lacked visibility into generative AI use in client-facing teams. We ran a structured discover, prioritize, and remediate cycle using the customer's existing security platforms.

  • Inventory of AI tools and integrations with risk tiering
  • Practical guardrails adopted without blocking billable work
  • Board-appropriate summary of residual exposure and next steps