🔐AI Security via Identity – Q&A with Proposed Approach

Securing AI in enterprise environments is crucial, and my research focuses on applying identity-driven controls.

This approach treats AI agents as managed identities, ensuring governed access, monitoring, and Zero Trust enforcement.

I have compiled key questions that every organization should consider regarding AI security. It’s essential to address these sooner rather than later.

I plan to evaluate solutions based on these questions and share my findings.

❓ 1. Do we know what AI systems exist in our environment?

Answer:

Most enterprises do not have full visibility of AI usage. AI agents, automation scripts, SaaS AI tools, and developer integrations often operate outside traditional governance (“shadow AI”).

Proposed approach:

• Build a central AI inventory (like CMDB for AI)

• Discover:

  • AI SaaS tools (Copilot, ChatGPT, etc.)

  • Internal AI apps and APIs

  • Automated workflows and agents

• Tag each AI system with:

  • Owner

  • Purpose

  • Risk classification

❓ 2. Are AI systems treated as identities?

Answer:

Traditionally, AI runs under service accounts or API keys, meaning:

• No accountability

• Shared credentials

• Poor traceability

Proposed approach:

• Treat every AI system as an identity (non-human identity)

• Assign:

  • Unique identity (not shared accounts)

  • Named owner (business accountability)

• Apply the same governance as:

  • Users

  • Devices

❓ 3. What access do AI systems have?

Answer:

AI agents often have broad or uncontrolled access, including:

• APIs (Graph, ServiceNow, etc.)

• Data stores

• SaaS platforms

This creates high-risk attack paths.

Proposed approach:

• Enforce least privilege access

  • Only grant what is strictly required

• Replace static credentials with:

  • Short-lived tokens

• Use:

  • Role-based or attribute-based access control

• Regularly review and certify access

❓ 4. How are AI systems authenticated?

Answer:

Many AI systems rely on:

• Hardcoded API keys

• Long-lived tokens

These are high-risk and difficult to rotate.

Proposed approach:

• Use modern authentication mechanisms, such as:

  • Token-based authentication

  • Dynamic credentials

• Eliminate:

  • Hardcoded secrets

  • Shared credentials

• Integrate AI systems into the same identity authentication framework as users and apps

❓ 5. Can we control what AI systems do?

Answer:

Without governance, AI systems can:

• Access data beyond intended scope

• Execute unintended actions

• Operate without oversight

Proposed approach:

• Define policy-based control:

  • What the AI can access

  • What actions it can perform

• Enforce centrally (not at app level)

• Apply Zero Trust principles:

  • Never trust, always verify

  • Validate every request

❓ 6. Do we have lifecycle control over AI systems?

Answer:

AI systems are often:

• Created ad hoc

• Left running indefinitely

• Never decommissioned

Proposed approach:

• Implement identity lifecycle management for AI:

  • Onboarding approval

  • Periodic review

  • Automated deprovisioning

• Introduce:

  • “Kill switch” capability for rapid shutdown

• Integrate into:

  • DevOps / release pipelines

❓ 7. Are AI activities monitored and auditable?

Answer:

In many environments:

• AI actions are not logged consistently

• No clear audit trail exists

Proposed approach:

• Log:

  • Authentication events

  • Access requests

  • Actions performed by AI

• Integrate with:

  • SIEM / monitoring platforms

• Enable:

  • Full traceability (who / what / when / why)

❓ 8. Can we detect and respond to AI-related threats?

Answer:

AI introduces new risks:

• Data exfiltration

• API abuse

• Rogue or compromised agents

Traditional controls may not detect these in real time.

Proposed approach:

• Implement continuous monitoring:

  • Behavioural analysis

  • Risk scoring

• Trigger automated responses:

  • Session termination

  • Access restriction

  • Step-up authentication

• Combine identity signals with:

  • Endpoint

  • Network

  • Application telemetry

❓ 9. How do we secure AI-to-system interactions?

Answer:

AI systems frequently interact across multiple applications and APIs, often with:

• Implicit trust

• Poor visibility

Proposed approach:

• Centralize control at the identity layer

• Secure:

  • API-to-API interactions

  • AI-to-app integrations

• Use:

  • Policy-based authorization

  • Scoped access tokens

• Eliminate:

  • Direct unmanaged connections

❓ 10. How does this align with Zero Trust?

Answer:

AI breaks traditional perimeter-based security:

• Systems act autonomously

• Decisions happen at machine speed

Proposed approach:

• Extend Zero Trust to AI:

  • Verify every request (user, device, AI)

  • Enforce identity-based access

  • Continuously evaluate trust

• Treat AI agents as high-risk identities by default

✅ Final Summary

Key principle:

AI security is fundamentally an identity and access problem.

Proposed enterprise model:

• Central identity layer governs:

  • Users

  • Devices

  • AI systems

User / Device / AI Agent ↓ Identity & Access Control Layer ↓ Apps / APIs / Data / AI Platforms

💡 Practical and technical takeaway for your role: EUC / endpoint / Intune responsibilities cover:

• Device compliance

• App deployment

• Conditional Access control based on signals (Human Identity authentication, authorisation and Device Status)

👉 This approach adds:

• Identity-driven control of AI behaviour

• Governance of AI access (not just apps)