Moderating AI in a Zero Trust World
Don Warden II
Executive in Cybersecurity | Expert in Incident Response & Digital Forensics
As artificial intelligence becomes more deeply integrated into our networks, workflows, and decision-making processes, one foundational security principle is being tested in new ways: Zero Trust. Born from the mantra “never trust, always verify,” Zero Trust has become a cornerstone of modern cybersecurity. But what happens when the actor we're verifying isn’t a user or device, but an AI system?
The Rise of Autonomous Agents
AI today isn't limited to dashboards and predictions. We’re seeing autonomous agents that can:
Ingest and interpret sensitive data.
Automate decision-making at scale.
Generate and distribute content internally and externally.
Interact with users and third-party systems via APIs.
This introduces new risks, not just from malicious actors manipulating AI systems, but from the AI itself making decisions without oversight, or worse, being exploited as a vector.
Why AI Demands a New Level of Moderation
In a Zero Trust model, every action must be authenticated, authorized, and continuously validated. The same must now apply to AI. But unlike users, AI agents:
Don’t have human judgment.
May not understand contextual boundaries.
Can process and act on vast amounts of data far faster than a human adversary.
The result? A need to moderate AI as both an asset and a potential insider threat.
Five Pillars of AI Moderation in a Zero Trust Model
Identity & Authentication - Every AI agent must have a verifiable identity. No anonymous processes. No implicit trust. Use workload identity, service principals, or mTLS-based trust to treat AI agents like privileged users.
Authorization & Least Privilege - AI should never have blanket access. Apply role-based access controls, time-bound tokens, and context-aware policies. What data can it access? What APIs can it invoke?
Real-Time Output Moderation - AI-generated content, whether code, language, or decisions, should be filtered in real-time for: Sensitive data leaks, Offensive or non-compliant language, Regulatory and brand alignment
Observability & Audit Trails - Logging AI actions is not optional. Whether the AI queried a database or executed a decision tree, its behavior must be transparent, traceable, and attributable.
Kill Switches & Guardrails - Just like you’d isolate a compromised endpoint, you need the ability to pause or terminate an AI process. Automated doesn’t mean uncontrolled.
Beyond Policy: Culture and Accountability
Moderating AI isn’t just about enforcement—it’s about redefining digital trust. Organizations need to:
Treat AI governance as an extension of their insider threat program.
Educate teams on when to trust, override, or question AI outputs.
Include AI behavior and drift detection in their SOC and IR playbooks.
Final Thoughts
AI isn't inherently trustworthy. It’s powerful, fast, and transformative. But like any powerful tool, AI must be controlled. Zero Trust provides the philosophy. AI moderation is the practice. Security teams that proactively moderate AI will not only avoid risk, they’ll build the kind of trust that lets AI thrive in mission-critical roles.