Trade vs. Cybersecurity: How AI Keeps You Safe

A single policy shift can change your threat model overnight.

That’s the uncomfortable lesson behind recent reporting that the US government may have backed away from sanctioning Chinese actors tied to the Salt Typhoon telecom intrusions while prioritizing trade negotiations. Pair that with easing export restrictions on high-end AI chips, and you get a message many security leaders hate hearing: cyber policy can be negotiable when economics enters the room.

If you’re responsible for enterprise security—or you support government missions—the practical takeaway isn’t to argue about diplomacy. It’s to accept a hard truth: you can’t outsource your security posture to geopolitics. In the “AI in Defense & National Security” series, this is a recurring theme: national strategy matters, but operational resilience is what keeps systems running when strategy gets messy.

When trade policy shifts, your threat exposure changes

Trade decisions don’t just affect prices and supply chains; they affect the behavior of adversaries and the constraints on defenders.

When sanctions, export controls, and regulatory posture become bargaining chips, organizations should assume three things will happen:

1. Adversaries keep operating. Nation-state intrusion and espionage programs are long-term investments, not short-term reactions.
2. Signals get interpreted. If punitive measures look “optional,” attackers may read that as tolerance—or at least as uncertainty.
3. Enterprises inherit the gap. Even if governments harden their own networks, most mission-critical services (telecom, cloud, software supply chain, logistics) are operated by private or quasi-private entities.

Salt Typhoon is a useful example because it targeted telecommunications providers—systems that sit underneath everything from enterprise connectivity to emergency services. Reporting has indicated the victim count has expanded dramatically over time, underscoring how one intrusion set can become a global risk multiplier.

The myth: sanctions deter cyber operations

Sanctions can create friction. They can make it harder to move money, to travel, to do business, and to recruit. But they don’t reliably stop intrusions.

Here’s what I’ve seen work better in practice (and it aligns with what many experts call deterrence by denial):

• Reduce attacker dwell time
• Make lateral movement expensive
• Detect persistence quickly
• Block data exfiltration paths
• Prove integrity with strong logging and controls

That’s not a diplomatic stance. It’s an engineering stance.

“Deterrence by denial” is a security strategy—and AI is how you scale it

Deterrence by denial means your systems are hardened enough that an attacker can’t get the payoff they want at a reasonable cost.

The problem is scale. Most organizations don’t have the humans to triage every alert, inspect every identity anomaly, and validate every supplier update across a sprawling environment.

This is where AI earns its keep—not as a buzzword, but as a force multiplier for security operations.

Where AI helps most (and where it doesn’t)

AI improves outcomes when it’s applied to high-volume, pattern-heavy workflows that overwhelm humans:

• Detection engineering at scale: surfacing unusual sequences (not just unusual events)
• Entity behavior analytics: spotting shifts in device/user/service-account behavior
• Phishing and social engineering defense: clustering campaigns, detecting linguistic and infrastructure reuse
• Triage and investigation support: summarizing timelines, correlating telemetry across tools
• Exposure management: prioritizing vulnerabilities based on reachable risk and observed exploitation

AI doesn’t fix:

• Poor asset inventory
• Weak identity controls
• Missing logs
• Unowned “shadow IT”

AI amplifies what you already instrument. If your environment is dark, AI just helps you move faster in the dark.

A practical stance: treat AI as the layer that turns “we have data” into “we have decisions.”

AI-driven geopolitical risk analysis belongs in security programs now

Most security programs still treat geopolitical risk as a quarterly slide, not an operational input.

That’s outdated. Geopolitical volatility is now a near-term driver of intrusion likelihood, especially for telecom, defense-adjacent manufacturing, critical infrastructure, and any enterprise with cross-border dependencies.

AI can help security teams translate geopolitical noise into usable actions.

What “AI geopolitical risk analysis” looks like operationally

It’s not a chatbot guessing the future. It’s a pipeline that:

1. Ingests signals (policy changes, sanctions news, export control shifts, incident reporting, takedowns, sector advisories)
2. Maps signals to your environment (your vendors, regions, subsidiaries, cloud regions, identity providers, telecom carriers)
3. Updates threat hypotheses (which actor sets, which techniques, which targets)
4. Pushes changes into controls (detections, conditional access policies, egress controls, supplier verification)

This matters because many compromises aren’t “zero-days.” They’re mismatches between the pace of change and the pace of internal governance.

A concrete example: trade changes and your AI supply chain

If high-performance AI chips, accelerators, or specialized hardware become easier to move across borders, you should expect:

• More AI-enabled phishing (higher personalization, faster iteration)
• More synthetic identity attacks (better voice/video artifacts and scripted interactions)
• Faster malware development cycles (automation for packers, lures, infrastructure churn)

Your response isn’t panic-buying tools. It’s tightening the boring stuff—identity, telemetry, egress, and supplier integrity—then using AI to keep up.

What CISOs and federal contractors should do in the next 30 days

If trade priorities can dilute cyber signaling, your organization needs a plan that doesn’t rely on external consistency.

Here’s a 30-day action list that’s realistic for most mature teams.

1) Treat telecom and identity as critical infrastructure

Salt Typhoon-style intrusions highlight a recurring weak point: communications layers and identity layers.

Do these now:

• Enforce phishing-resistant MFA for admins and privileged users
• Audit service accounts and non-human identities (rotation, scope, and ownership)
• Reduce reliance on “trusted network” assumptions (especially for remote admin paths)

2) Build an AI-assisted “investigation cockpit”

You don’t need full autonomy. You need speed and consistency.

Minimum viable capabilities:

• Automatic timeline reconstruction for incidents
• AI-assisted alert deduplication and clustering
• Natural-language querying across logs (with guardrails and auditability)

The win is simple: fewer one-hour investigations that should’ve taken ten minutes.

3) Prioritize vulnerabilities by exploitability, not severity

If your VM program is still driven by CVSS alone, you’ll always be behind.

Add:

• Asset criticality
• Exposure (internet-facing, reachable paths)
• Observed exploitation and attacker tooling
• Compensating controls (WAF, segmentation, EDR coverage)

AI helps by ranking what’s most likely to be used against your environment, not what’s most alarming on paper.

4) Implement “deterrence by denial” metrics

If you can’t measure it, it won’t survive budget season.

Track:

• Median time to detect (MTTD)
• Median time to contain (MTTC)
• Privileged access review cadence
• % of critical assets with complete logging
• Egress control coverage for sensitive data stores

These metrics tell a clear story to boards and agency stakeholders: you’re reducing payoff for attackers.

How AI bridges the gap between economic goals and security outcomes

Organizations often get forced into false choices:

• “We need global growth, so we accept more risk.”
• “We need security, so we slow down the business.”

There’s a better way to approach this: use AI to make risk decisions faster and more precise.

The business value security leaders can actually sell

AI-driven security programs can align with economic and mission goals by:

• Reducing incident cost through faster containment
• Lowering false positives so analysts work real threats
• Improving audit readiness with consistent evidence trails
• Keeping cross-border operations running with adaptive controls

This is especially relevant for defense contractors and regulated industries facing frameworks like CMMC 2.0-style expectations: control is one thing; provable control at scale is another. AI helps with the “provable” part when implemented with logging, governance, and model oversight.

People also ask: does AI make nation-state attacks worse or easier to stop?

Both—and pretending it’s only one is a mistake.

AI lowers the cost of producing convincing lures, automating reconnaissance, and iterating on malware infrastructure. At the same time, AI improves defensive speed: correlation, anomaly detection, investigation support, and exposure prioritization.

The deciding factor is discipline.

• Teams with strong identity, logging, and response muscle get faster.
• Teams without those basics just get overwhelmed faster.

What this means for the AI in Defense & National Security narrative

Defense and national security conversations often focus on capabilities—offense, surveillance, autonomy. But resilience is the quieter story that decides outcomes.

If trade negotiations reshape cyber posture at the national level, enterprises should assume the burden shifts toward self-reliant defense. Sanctions may come and go. Adversary campaigns won’t.

If you want a practical starting point: aim for deterrence by denial, then use AI to scale it. That’s how you keep operations stable even when policy signals are inconsistent.

The next question worth asking isn’t whether trade concerns are trumping cybersecurity. It’s this: if your environment became the next bargaining chip, would your defenses still hold?