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TL;DR
- Shadow AI is unsanctioned AI use without formal approval or oversight, and it behaves like shadow IT, just faster and more data-extractive.
- Your discovery plan must cover AI usage inside SaaS, browser-based tools, API driven connections, and emerging MCP tool gateways.
- Build a two-lane response: low risk enablement path and high-risk containment path.
What Shadow AI looks like in real organizations
Shadow AI commonly starts as “helpful shortcuts” that bypass review:
- Employees using public AI tools for sensitive drafting and summarization
- Enabling AI features inside SaaS tools without security review
- Connecting copilots to drives, tickets, CRM, or code systems
- Spinning up agents that can take actions with broad permissions
Shadow AI Is unsanctioned use of AI tools without formal approval or oversight, and ISACA frames it as a shadow IT like pattern that bypasses controls.
The Shadow AI discovery playbook
Step 1: Define “approved vs unapproved”
- Create an allowlist of approved AI services, models, and connectors.
- Define “restricted data classes” that must never be used in unapproved AI tools.
- Publish a fast approval path so teams do not route around you.
Step 2: Inventory AI features inside existing SaaS
- Identify SaaS apps with built-in AI features and whether they are enabled.
- Review tenant settings, admin toggles, and user-level enablement.
- Track where AI features can access data, such as tickets, docs, CRM records.
Step 3: Detect unsanctioned AI tools and browser usage
- Monitor access patterns to common AI endpoints at the proxy or gateway layer where possible.
- Look for large copy paste events, uploads, and unusual traffic spikes from corporate networks.
- Pair monitoring with policy and education, since some usage is invisible by design.
Step 4: Map identity and token sprawl
- Find API keys, OAuth grants, and tokens tied to AI apps or AI features.
- Identify over privileged scopes and long lived tokens.
- Disable shared keys and move to scoped, expiring credentials.
Step 5: Discover agentic workflows and tool access
- Search for automation frameworks and agent runners.
- Audit what tools agents can call, and what data sources they can reach.
- Flag any agent that can mutate state without approvals.
Step 6: Include MCP in your discovery scope
MCP standardizes how AI apps connect to tools and data sources through MCP servers. That also means it becomes a new integration surface that can appear quickly inside teams. Treat MCP servers as discoverable assets, not as “just dev tooling.”
Step 7: Triage findings with two lanes
Lane A: Enablement
- Approved tools, correct data handling, minimal access scopes
- Document the pattern and scale it safely
Lane B: Containment
- Unapproved tools touching sensitive data
- Over-privileged tokens, broad SaaS grants
- Agents with write access and no approvals
Containment actions can include removing tokens, restricting egress, disabling SaaS AI features, and issuing incident-style comms.
Step 8: Make Shadow AI part of AISPM
Shadow AI is not a one-off cleanup. Fold it into AI Security Posture Management:
- Continuous discovery
- Policy baselines
- Identity and data mapping
- Remediation workflows
This aligns to the continuous lifecycle expectation in NIST AI RMF and ISO/IEC 42001 governance patterns.
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