Zero Trust With AI: Secure Access Without Losing Speed

Zero Trust has a reputation problem in defense circles. People hear “verify everything” and picture mission teams stuck behind endless prompts, approvals, and locked-down data silos. Meanwhile, adversaries aren’t waiting—cyber operations, disinformation, and data theft all move at machine speed.

The DoD’s Zero Trust Architecture (ZTA) roadmap (with compliance targets through fiscal year 2027) is trying to solve a real operational paradox: protect sensitive data from manipulation or exfiltration while keeping it fast and usable for the people who need it. If your implementation slows decisions, you don’t have information dominance—you have “secure confusion.”

This post is part of our AI in Defense & National Security series, and the stance here is simple: Zero Trust only works at defense scale when AI and automation handle the volume. Humans should approve exceptions and set policy intent—not triage millions of daily signals.

Zero Trust is an operational mandate, not a compliance project

Zero Trust succeeds when it’s treated as mission assurance, not paperwork.

Most organizations start by mapping controls and chasing checklists. That’s understandable—DoD guidance includes a large set of required activities and target goals. But if you stop there, you’ll build a brittle environment: secure on paper, slow in practice.

A mission-ready Zero Trust program behaves differently:

• It assumes compromise and designs for containment.
• It prioritizes identity, device health, and data sensitivity over network location.
• It measures success in decision latency and blast radius, not only audit artifacts.

A useful way to phrase the goal is:

Zero Trust should reduce the time to trust the right action—and increase the time to exploit the wrong one.

That “time to trust” metric is where AI becomes essential.

The real enemy: either/or thinking

The false choice is “strong security” versus “fast operations.” Defense networks need both. The balance comes from risk-adaptive access: higher assurance when the context is risky, and smoother access when the context is normal and validated.

That approach is hard to do manually. It requires continuous evaluation of:

• Who the user is (and whether they’re behaving normally)
• What device they’re on (and whether it’s healthy)
• What data they’re touching (and its mission impact)
• Where the request originates (network, facility, time, role)
• What’s happening in the threat environment right now

Humans can’t keep up with that tempo. Machines can.

AI makes Zero Trust usable at mission tempo

AI’s best contribution to Zero Trust isn’t “fancy detection.” It’s making security decisions fast, consistent, and explainable enough that operators don’t fight the system.

Done well, AI supports three practical outcomes:

1. Continuous verification without constant friction (less prompting, more silent validation)
2. Faster detection-to-response via automation and orchestration
3. Better prioritization—analysts spend time on the small number of events that actually matter

A lot of security programs fail because they confuse “more alerts” with “more security.” In defense and national security, alert overload becomes an operational vulnerability: important signals get buried.

User behavior monitoring: the “credit score” concept for access

One pragmatic pattern is establishing behavioral baselines—what “normal” looks like for a specific user, mission team, or system—and then flagging deviations that correlate with risk.

Think of it like a credibility profile:

• Normal hours vs. unusual access windows
• Typical applications and datasets vs. new, sensitive targets
• Usual facility/network patterns vs. surprising geography or enclave hopping
• Standard workflow sequences vs. automation-like bursts that look like scripting

When a deviation occurs, the system can step up security (additional authentication, reduced permissions, forced re-verification, session isolation) without shutting down legitimate work across the board.

The payoff: mission teams keep moving, while true anomalies get spotlighted.

A practical service model: data, response, ops, and threat intel

One reason Zero Trust programs bog down is that teams buy tools in isolation—identity here, endpoint there, SIEM somewhere else—then discover integration is the real project.

A service model approach (like BAE Systems’ Velhawk, described publicly with four “Wings of the Watch”) maps cleanly to how defense organizations actually operate. Whether you use that solution or build your own, the structure is worth copying.

Data mastery and visibility: start with “what happened?”

A Zero Trust program can’t function without observability. The baseline requirement is high-quality telemetry across identity, device, network, application, and data layers.

In practice, that means:

• A data platform that can handle security telemetry at scale
• Normalization so events can be correlated across domains
• Data governance so analysts trust the outputs
• Analytics that surface why an alert matters (context), not just that it happened

If you’re working toward information dominance, the goal isn’t simply logging. It’s this:

Every security decision should be backed by a traceable story—who did what, from where, on what asset, to which data.

Incident response with automation: speed beats perfect

Zero Trust reduces the chance of a major breach; it doesn’t eliminate incidents. When something slips through, the decisive factor is time to contain.

Automation supports:

• Rapid isolation of a device or identity session
• Automated enrichment (pulling related identity/device/data context)
• Triage routing based on mission criticality
• Repeatable containment playbooks (so response is consistent at 2 a.m.)

Pair that with digital forensics and you get not only response, but learning: what tactics, techniques, and procedures were used—and what control should be tightened next.

Security operations and continuous authorization: the underrated ROI

Most leaders want Zero Trust because of risk. Most teams struggle because of bureaucracy.

A mature path uses automation to reduce the overhead of governance, risk, and compliance. The idea of continuous Authority to Operate (ATO) matters here: frequent assessment and evidence generation, not episodic, painful “ATO events.”

If you can automate evidence collection, configuration validation, patch posture, and control attestation, you get two benefits:

• Faster delivery of mission software (DevSecOps without the bottleneck)
• Fewer manual compliance fire drills (real cost savings)

This is where formal methods analysis and AI-assisted assessment can be genuinely useful: not as buzzwords, but as accelerators for verifying that systems meet required security properties.

Threat intelligence and proactive defense: stay ahead of the pattern

Reactive security is expensive. Proactive security is cheaper and calmer.

Threat intelligence fused with internal telemetry helps you answer:

• Which adversary behaviors match what we’re seeing internally?
• Which assets are most likely to be targeted next?
• Which controls should be tuned based on current campaigns?

When done responsibly, AI can support predictive analysis—flagging emerging attack paths—so you can harden systems before the next exploit chain lands.

How to implement Zero Trust without slowing the mission

Zero Trust succeeds when you engineer for two goals at once: minimum blast radius and minimum decision latency.

Here’s what I’ve found works in real programs: build around outcomes, then map to pillars.

1) Define “mission friction” as a first-class metric

If you don’t measure friction, you’ll create it.

Track:

• Time from access request to access granted
• Number of prompts per user per day (by role)
• False positive rate for step-up authentication
• Mean time to contain (MTTC) for high-severity incidents

Then set boundaries: if an added control increases access time beyond your operational threshold, it must be redesigned or automated.

2) Start with the data that actually matters

Not all data is equal. Zero Trust programs fail when they try to label everything at once.

Do this instead:

1. Identify the top mission workflows (e.g., targeting cycle support, intel fusion, logistics readiness)
2. Map the datasets those workflows depend on
3. Classify and tag those datasets first
4. Apply least-privilege and continuous monitoring there before expanding

This creates immediate value and avoids a multi-year “taxonomy project.”

3) Make policy intent human, and enforcement machine

Humans should decide:

• Which roles can access which data under which mission conditions
• What “abnormal” means for high-risk teams
• What the acceptable tradeoffs are in degraded modes

Machines should handle:

• Continuous evaluation of context
• Real-time policy enforcement
• Automated evidence and reporting
• Alert correlation and prioritization

That division of labor is how you keep both security and speed.

4) Design for hybrid reality

Defense organizations aren’t moving to a single environment. They’re managing secure hybrid setups: on-prem enclaves, tactical edge, cloud, coalition networks, and partner systems.

Zero Trust must work across boundaries. That requires:

• Identity federation patterns that don’t collapse under coalition complexity
• Device posture checks that work at the edge
• Data-centric controls (encryption, tokenization, attribute-based access)
• Interoperability standards so telemetry is comparable

If your Zero Trust design assumes perfect connectivity and uniform infrastructure, it will fail the first time a mission depends on degraded comms.

People also ask: what does “information dominance” mean in a Zero Trust world?

Information dominance means your decision-makers get trusted information faster than the adversary can disrupt it. In practice, that’s three things:

• Integrity: adversaries can’t quietly alter what commanders rely on
• Availability: authorized users can access data when timing matters
• Confidence: users trust the data lineage and system behavior

Zero Trust contributes by shrinking lateral movement and enforcing least privilege. AI contributes by making verification continuous and scalable.

Where this is heading in 2026: Zero Trust becomes “default,” not “project”

By late 2025, the direction is clear: Zero Trust is moving from architecture diagrams into operational expectations. The organizations that will look calm in 2026 are the ones building now toward:

• Continuous monitoring that’s explainable to humans
• Automated response that’s safe-by-design
• Data governance that supports interoperability across mission partners
• Continuous authorization models that keep software delivery moving

The uncomfortable truth is that manual Zero Trust doesn’t scale. If your program depends on heroic analysts and weekly war rooms, it won’t survive the next surge.

If you’re responsible for cyber resiliency in defense, national security, or critical government services, the practical next step is to assess two gaps: (1) where you lack visibility and (2) where you lack automation. Those gaps are where mission tempo goes to die.

The question worth ending on is the one teams avoid because it’s blunt: If your network was partially compromised tomorrow, would your people still get the right data fast enough to win?