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Know where AI and agent exposure exists

AI usage is spreading faster than most security programs can govern it. Microsoft 365 Copilot is rolling out, custom agents are being built, and employees are using public AI tools with corporate data. In many environments, the actual state of AI security is unclear. This Sprint provides an evidence-based view of where AI and agent exposure exists today and what matters most to address first.
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What is at stake

AI is the fastest-evolving risk surface in the modern enterprise. It is also one of the hardest to assess accurately, because exposure spans data security, identity, governance, application behavior, and operational guardrails across capabilities that are evolving faster than most security programs can absorb.

Most organizations are adopting AI while trying to govern it at the same time. Copilot is licensed before data exposure is fully understood. Employees use public AI tools to move faster. Agents are built with access patterns security has not reviewed. This Sprint replaces assumption with evidence.

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    AI usage exists beyond what security can see end to end
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    Sensitive data may reach AI systems without consistent awareness or control
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    Agents operate with permissions that were never reviewed or narrowed
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    Guardrails exist on paper but not consistently in enforcement
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    AI adoption outpaces governance and monitoring
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    Leadership lacks a defensible AI exposure position for audits, regulators, or incident review

    • Why AI exposure is difficult to answer with confidence

    AI usage visibility

    AI activity spans Copilot, embedded AI features, custom agents, and public tools, but telemetry and investigation paths are fragmented or incomplete.

    Data exposure to AI systems

    Sensitive data can reach AI systems through prompts, overshared source content, plugins, connectors, and public tools when data protection controls are not adapted to AI risk.

    Agent identity, access, and governance

    Agents are created with broad or standing permissions, unclear ownership, and limited lifecycle governance, creating unmapped access paths.

    Governance and operational guardrails

    AI policies exist, but enforceable controls, escalation paths, and cross-functional ownership are inconsistent or undefined in practice.
    How the Assessment runs

    A defensible way to assess AI and agent exposure

    The AI and Agent Exposure Assessment Sprint evaluates AI exposure based on observable usage, access paths, data exposure patterns, and operational guardrails. Rather than relying on policy intent, it assesses how AI is actually being used and governed today.

    What you recieve.

    At the conclusion of the Sprint, you have a clear, evidence-based understanding of AI and agent exposure and a practical plan to reduce the risks that matter most.

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    Prioritized AI security findings across usage visibility, data exposure, agent governance, and guardrails
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    An AI exposure map showing observed AI usage, sensitive data exposure patterns, and areas of concentrated risk
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    A governance and guardrail effectiveness summary highlighting gaps between policy and practice
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    A target-state remediation roadmap sequenced by exposure reduction value and governance criticality
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    A leadership-ready AI exposure summary suitable for executives, auditors, regulators, and planning stakeholders

    • What comes after?

    The Sprint produces clarity and a roadmap. Execution of that roadmap is a separate decision.

    Depending on what the Sprint uncovers, next steps may include an AI and Agent Security Foundation, a SOC AI Foundation for Security Copilot operating model and governance, or an Optimize engagement focused on AI guardrails, agent governance, and ongoing exposure reduction.

    Whether the next step is with Lockbase or your internal team, the value of the Sprint is a defensible plan and the ability to act on it.

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