Extending Zero Trust to AI: What Federal Civilian Agencies Can Do Now

This is the final post in a three-part series on AI security and governance for Federal Civilian agencies. The first post covered why M-25-21 changes the operating landscape. The second post introduced a six-part framework for secure AI adoption.

Bottom line up front: Federal Civilian agencies have spent years moving toward Zero Trust. AI should be part of that strategy. Zscaler helps agencies extend Zero Trust principles into AI interactions across employee use, agency-built applications, and cloud environments. The practical starting point is visibility, followed by data protection, guardrails, testing, and governance evidence.

AI creates new interaction points that Zero Trust must cover

Federal agencies have spent years moving toward Zero Trust. AI should be part of that strategy.

AI introduces new interaction points: prompts, responses, models, agents, AI APIs, embedded AI, AI-enabled SaaS, coding assistants, RAG pipelines, vector databases, MCP servers, tool-calling workflows, and agency-built AI applications.

Each one can become a path for data exposure, unauthorized access, manipulation, or mission risk.

A Zero Trust approach to AI asks practical questions. Who is using the AI system? What application or model are they accessing? What data is being shared? Is the user authorized? Is the tool approved? Is the destination trusted? Is sensitive data involved? Is the AI response safe and appropriate? Is the AI system acting within its mission scope? Is the interaction logged and governed? Can policy be enforced in real time?

Zscaler helps agencies extend Zero Trust from users, devices, applications, branches, and workloads into AI interactions. This includes securing employee AI use, discovering AI assets, protecting sensitive data, applying guardrails, testing agency-built AI applications, and monitoring AI behavior over time.

Where Zscaler fits

Zscaler's role is to help Federal Civilian agencies make secure AI adoption operational. The platform supports three main areas.

AI asset management helps discover and inventory AI use across endpoints, traffic, SaaS, code, and cloud environments. This supports governance, inventory, visibility, and risk prioritization.

Secure access to AI applications helps protect employee use of public GenAI and embedded AI applications through access control, DLP, browser isolation, prompt inspection, response inspection, and AI Guard. This supports acceptable-use policy enforcement and sensitive data protection.

Secure AI applications and infrastructure helps protect agency-built AI applications, agents, and model interactions through proxy or API-based guardrails, AI red teaming, model benchmarking, prompt hardening, and continuous monitoring. This supports public trust, mission alignment, and risk management for higher-impact AI systems.

These capabilities help agencies answer the questions at the center of M-25-21 compliance: What AI is being used? What data does it touch? Who is using it? What risks exist? What controls are applied? What systems have been tested? What evidence can we provide? And how do we enable AI safely at mission speed?

What this looks like across missions

Secure AI adoption will look different depending on the agency's mission.

A benefits agency may want to use AI to help employees summarize case information, while preventing exposure of PII or unauthorized eligibility guidance.

A public health agency may want to use AI to synthesize research, but needs responses grounded in authoritative sources.

A regulatory agency may want to use AI to assist with inspections, investigations, or compliance reviews, while protecting sensitive regulated-entity information and avoiding unsupported conclusions.

A grants-making agency may want to improve application processing, but must protect applicant data, procurement-sensitive information, and pre-decisional materials.

A citizen services agency may want to deploy a public-facing AI assistant, but needs to ensure the assistant provides accurate information, avoids unauthorized determinations, and escalates to a human when appropriate.

A software development organization may want to use AI coding assistants to accelerate modernization, while preventing source code leakage, secrets exposure, and unapproved model use.

A security operations team may want to use AI to improve triage, detection, and response, while maintaining human oversight and protecting sensitive incident data.

These are the kinds of use cases where AI security, Zero Trust, and governance need to work in concert.

A practical path forward

A good starting point is visibility.

Federal Civilian agencies can begin by identifying the AI already in use across public GenAI, embedded AI, desktop tools, developer environments, cloud services, models, agents, and AI-enabled applications.

From there, agencies can map AI usage to data sensitivity and mission risk. This means understanding where AI interactions involve sensitive mission data, including PII, CUI, source code, benefits data, health data, financial data, law enforcement data, regulatory data, or procurement-sensitive information.

Once agencies understand the risk, they can enforce acceptable-use and data protection policies. This may include access controls, DLP, browser isolation, prompt inspection, response inspection, and user coaching.

For agency-built AI applications, agencies should apply guardrails, proxy or API-based inspection, and red teaming before deployment. After deployment, they should continuously monitor usage, policy violations, data exposure attempts, red team findings, model behavior, and emerging AI assets.

Finally, agencies need to report what they learn to governance stakeholders, including the CAIO, AI Governance Board, CIO, CISO, privacy, legal, data, acquisition, and mission leaders.

This approach supports the intent of M-25-21: move faster with AI while maintaining governance and public trust.

Where this leaves us

AI is becoming part of how Federal Civilian agencies deliver their missions.

But responsible adoption requires more than enthusiasm and policy statements. Agencies need visibility, data protection, security controls, testing, monitoring, and governance evidence.

M-25-21 sets the direction: accelerate AI adoption, remove barriers, establish governance, manage risk, and preserve public trust.

Zscaler helps agencies put that direction into practice by extending Zero Trust into AI interactions. This means helping agencies discover AI use across the enterprise, reduce shadow AI risk, protect sensitive government data, enable approved GenAI tools, govern embedded AI in SaaS, secure developer use of AI, identify AI assets in cloud environments, protect agency-built AI applications and agents, red team AI systems before deployment, apply guardrails for prompts and responses, monitor AI usage continuously, and provide evidence to AI governance stakeholders.

The goal is AI adoption with the visibility, control, and governance needed to manage risk in real time, at mission speed.

To learn more about how Zscaler extends Zero Trust to AI for Federal Civilian agencies, reach out to your Zscaler account team for a detailed overview of our AI Security capabilities.

Read the full series: Part 1: M-25-21 Changes the AI Conversation | Part 2: A Practical Framework for Secure AI Adoption