AI-Ready Cyber Policy Shifts for National Security

A national cyber strategy can say all the right things—and still fail if the “bedrock” policies underneath it don’t match how attacks actually happen.

That’s why the Trump administration’s plan to revisit core U.S. cyber policy frameworks (the documents that define who can act, when, and under what authority) matters to anyone building, buying, or governing security in the public sector. If you lead cybersecurity, data, AI, IT, acquisition, or mission operations, these updates won’t be abstract Washington process. They’ll shape how quickly agencies can respond, what counts as “acceptable risk,” and how tightly AI-driven systems will be monitored.

This post is part of our AI in Defense & National Security series, where we focus on practical realities: how AI improves detection, triage, and resilience—and how governance keeps those systems safe, lawful, and auditable.

What “revisiting bedrock cyber policies” actually changes

The short version: updating foundational cyber policies changes the speed, scope, and coordination of U.S. cyber operations—on both offense and defense.

The RSS report describes plans to reexamine several frameworks commonly viewed as the “rules of the road” for national cyber action:

• NSPM-13 (classified): how cyber operations are authorized and by whom.
• PPD-41: how the federal government coordinates during significant cyber incidents (roles, lead agencies, and interagency structures).
• NSM-22: standards and expectations for critical infrastructure cybersecurity across sectors.

If these are revised alongside a new cyber strategy, agencies should expect changes in:

1. Authority and escalation paths (who can approve what, how fast decisions move).
2. Incident command mechanics (how “major incident” is declared, who leads, what data must be shared).
3. Critical infrastructure expectations (what “good” looks like for operators, and how compliance burden is defined).

Here’s my take: policy updates are where strategy becomes real. They determine whether an agency can act in hours—or spends days arguing about which office owns the problem.

The offensive pillar: “preemptive erosion” meets AI reality

The strategy described includes an offensive pillar aimed at “preemptive erosion” of foreign adversaries’ hacking capacity. That phrase is doing a lot of work.

What “preemptive erosion” means in operational terms

Operationally, it suggests a posture that’s less reactive and more focused on:

• Disrupting adversary infrastructure earlier (command-and-control nodes, staging servers)
• Raising adversary cost (forcing tool changes, burning access)
• Narrowing adversary options (reducing dwell time and persistence)

This approach becomes credible only if the government can do three things consistently:

1. Attribute with confidence (who’s behind it and why you’re sure)
2. Act quickly (before infrastructure changes, access disappears, or malware spreads)
3. Coordinate with private sector visibility (because many indicators live in commercial networks)

AI is central to all three—but it’s also a source of new risk.

Where AI helps—without turning cyber operations into chaos

AI can support this offensive posture in bounded and accountable ways:

• Threat intelligence fusion: correlating telemetry across sensors, logs, and reports to identify common infrastructure and operator tradecraft.
• Entity resolution: linking domains, IPs, certificates, code similarity, and behavioral signatures to a consistent adversary cluster.
• Decision support: prioritizing disruption candidates based on mission impact, likelihood of collateral effects, and confidence scores.

The trap is assuming AI equals autonomy.

If policy shifts encourage more aggressive operations while agencies are simultaneously experimenting with AI-driven targeting and correlation, governance must be explicit about:

• Confidence thresholds for action (what score is “good enough” and who owns the risk)
• Human review gates for disruptive actions
• Auditability (what the model saw, why it recommended, and what was decided)

A fast cyber operation with weak attribution isn’t “preemptive.” It’s a liability.

The private-sector question: visibility vs. authority

The article notes ongoing debate about private sector participation in offensive efforts. The practical issue isn’t whether companies are patriotic. It’s whether the system can cleanly separate:

• Intelligence sharing and technical collaboration (highly useful, scalable)
• Direct authority to conduct offensive cyber activity (legally and geopolitically fraught)

AI-driven data sharing can make collaboration more powerful—especially if it standardizes indicators, confidence, and context. But the more operational the collaboration gets, the more important it becomes to define:

• permissible actions
• liability boundaries
• deconfliction procedures
• retention and privacy rules

Without that, “partnership” devolves into confusion during the first crisis.

Defensive priorities: zero trust and quantum-safe aren’t optional anymore

The strategy also emphasizes zero trust and quantum-safe security. Those are smart calls—and they’re overdue.

Zero trust: the governance problem disguised as an architecture problem

Zero trust is often pitched as a technology pattern. In federal environments, it’s equally a policy enforcement pattern:

• Every access request is evaluated (identity, device posture, location, behavior)
• Privileges are minimized and time-bound
• Segmentation limits blast radius

AI can make zero trust more effective by identifying risky behavior patterns (impossible travel, abnormal data access, anomalous admin activity). But it also introduces a governance requirement agencies tend to underestimate: when AI blocks access, you need due process.

In other words, build:

• clear appeal paths (who can override and under what conditions)
• transparent “reason codes” for denials
• monitoring for bias against certain roles, locations, or mission profiles

That’s not bureaucracy. It’s operational continuity.

Quantum-safe security: start with inventory, not algorithms

Quantum-safe migration is often treated as a cryptography problem. It’s first a system inventory problem.

A credible quantum-safe plan starts with:

1. Cataloging where cryptography is used (VPNs, databases, identity, APIs, PKI, embedded systems)
2. Finding long-lived secrets (data that must remain confidential for 10–25 years)
3. Prioritizing high-impact pathways (identity and key management first)

AI can accelerate this by scanning codebases, configs, and network flows to detect cryptographic libraries and protocols at scale—especially across sprawling federal estates.

Critical infrastructure: regulation relief can’t mean risk denial

One pillar described is reforming cybersecurity regulations to reduce compliance burden. Agencies and operators want this, and I sympathize: checkbox compliance is expensive and often misses real risk.

But here’s the hard truth: if “burden reduction” becomes “evidence reduction,” critical infrastructure gets weaker.

A better stance: fewer controls, stronger proof

The way out is to shift from document-heavy compliance to evidence-backed controls that can be validated continuously.

AI can help regulators and operators meet in the middle:

• Continuous control monitoring (patch status, MFA enforcement, privileged access changes)
• Risk scoring tied to real telemetry rather than policy binders
• Automated reporting that shows what changed, when, and what was remediated

For critical infrastructure, this is especially relevant during winter peak demand (December is a reminder). Outages and disruptions in power, water, transit, and healthcare aren’t theoretical. They cascade fast.

The goal shouldn’t be “less compliance.” It should be “less paperwork, more measurable security.”

Federal network modernization: AI can reduce mean time to respond—if data is usable

Modernizing federal networks is a recurring strategy pillar because agencies still struggle with:

• legacy identity stores
• inconsistent logging
• brittle network segmentation
• decentralized IT ownership

AI security tools don’t fix those issues by themselves. They amplify what you feed them.

The minimum viable foundation for AI-driven cyber defense

If you want AI to reduce mean time to detect (MTTD) and mean time to respond (MTTR), you need consistent security data. Practically, that means:

1. Unified identity: one way to understand a user across systems.
2. Normalized logs: standard fields and timestamps across major sources.
3. Asset truth: a reliable inventory of endpoints, servers, cloud resources, and owners.
4. Playbooks that match policy: automated response actions mapped to authority.

That last point is where policy revisions (like PPD-41 coordination rules) directly influence technical outcomes. If your playbook says “isolate system” but your governance says “must notify X before isolating,” automation will stall—or worse, violate procedure.

Cyber workforce: AI doesn’t replace talent, it changes what “good” looks like

The strategy’s workforce pillar mentions incentives, a potential cyber academy concept, and a business-driven talent pipeline.

I’m strongly in favor of this direction, but it needs a modern definition of “cyber talent.” Many agencies still hire as if every analyst must manually sift logs all day.

The new baseline: analysts who can supervise machines

In AI-enabled SOCs and mission environments, the high-value skills shift toward:

• validating model outputs and spotting hallucinated correlations
• tuning detections to mission context
• translating technical findings into operational decisions
• managing data quality and access controls

A cyber academy (or any workforce program) should include:

• AI incident triage (how to trust, verify, and document)
• prompting and workflow design for secure analyst assistants
• model risk management (testing, drift monitoring, red teaming)
• policy literacy (what your authority is under incident conditions)

If you don’t teach policy alongside AI, you’ll get faster analysis and slower decisions—a frustrating combination.

Practical next steps for public sector leaders (next 90 days)

If policy updates and a new strategy are imminent, you can act now without guessing final text.

1. Map your incident process to PPD-41-style roles

   • Identify who declares a major incident, who leads comms, and who owns remediation decisions.

2. Build an “AI oversight layer” for cyber tooling

   • Document which models are used, what data they see, who can change them, and how outputs are audited.

3. Prepare for offensive-defensive intelligence convergence

   • Tighten handling rules for threat intel, ensure tagging, and define what can be shared across mission partners.

4. Start quantum-safe discovery

   • Inventory cryptographic use and identify long-lived sensitive data. Don’t wait for perfect standards to begin.

5. Turn compliance into telemetry

   • Replace quarterly screenshots with continuously collected evidence wherever possible.

Where this goes next for AI in defense and national security

Revisiting NSPM-13, PPD-41, and NSM-22 isn’t just a paperwork exercise. It’s a bet on a faster, more operationally integrated cyber posture—one that increasingly depends on AI for speed, correlation, and scale.

The risk is obvious: aggressive posture plus AI-driven automation without crisp governance can produce confident mistakes at machine speed. The opportunity is bigger: policy that explicitly supports AI-enabled oversight, auditable decisioning, and measurable resilience.

If you’re responsible for mission systems or public services, ask a straightforward question as these changes roll out: Can we prove why we acted, what data drove the decision, and who approved it—within 24 hours of an incident? If the answer is no, policy reform won’t save you. Better operational plumbing will.

Want help pressure-testing AI governance for cyber operations—before the next strategy memo becomes an audit finding? That’s a conversation worth having.