EXECUTIVE GUIDE
The Executive's Guide to Shadow AI
Detection. Governance. Built from evidence, not questionnaires.
+$670K
Per-breach premium for high shadow-AI use
IBM 2025
97%
Of AI-system breach victims lacked proper access controls
IBM 2025
63%
Of breached organizations have no AI governance policy
IBM 2025
89%
Of corporate GenAI usage happens outside any policy
Verizon DBIR 2025
EXCERPT
A preview of what is in the guide.
What Shadow AI actually is
Any artificial intelligence tool, model, API, or agent operating in your environment without formal organizational approval, procurement review, or security assessment. Shadow IT took years to become a boardroom issue. AI is moving on a different timeline. The barrier to deployment is a package install and an API key.
The four detection layers
Effective Shadow AI detection requires layered coverage: platform connectors (sanctioned usage), CASB and network detection (personal API keys on managed devices), code and dependency scanning (AI SDKs in source repos), and endpoint application inventory (locally running models). No single tool catches everything. Each layer catches what the others miss.
Detection is not governance
Knowing Shadow AI exists in your environment and governing it are two separate problems. Governance requires four things detection does not provide on its own: an inventory with accountable owners, a documented risk assessment for each system, technical controls that enforce what the policy commits to, and evidence the program is functioning over time.
The agentic AI problem is different in kind
An employee using ChatGPT is a point-in-time risk. An autonomous agent that reads your support queue, drafts responses, accesses your CRM, and sends emails on behalf of your company is an ongoing operational risk with a blast radius that has not been scoped, reviewed, or formally approved. Agentic governance requires tool permissions, escalation policies, and execution audit logs.
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