Trade Politics vs Cybersecurity: AI Helps Close Gaps

A single diplomatic decision can change your threat model.

In December 2025, reports suggested the US government backed away from sanctioning China’s Ministry of State Security for its alleged role in the Salt Typhoon telecom intrusions, while trade talks and chip-export considerations stayed in motion. Whether you agree with the politics or not, the operational lesson is blunt: nation-state cyber activity doesn’t pause for negotiations, and your defenses can’t depend on policy consistency.

This post is part of our “AI in Defense & National Security” series, where we look at the messy real-world intersection between strategy, mission outcomes, and security operations. Here, the takeaway isn’t “sanctions good” or “sanctions bad.” It’s this: policy tools are slow and negotiable; security controls must be fast, measurable, and resilient. AI-driven cybersecurity is one of the few practical ways to close that gap.

When trade policy becomes part of your threat model

Answer first: When cybersecurity is treated as a bargaining chip in trade negotiations, organizations inherit volatility they didn’t choose—so they need defenses that assume adversaries will keep probing regardless of public policy.

The Salt Typhoon reporting sits inside a broader pattern: cyber sanctions, export controls, and regulatory pressure can be applied—or eased—based on diplomatic needs. Experts quoted in the source article point out that this can make cyber consequences look negotiable. And adversaries notice negotiability.

For CISOs and security leaders, this changes the planning baseline:

• Deterrence signals become inconsistent. If sanctions are lifted, delayed, or swapped for other concessions, attackers learn to wait out political cycles.
• Regulatory pressure can swing. The same month one administration tightens telecom security expectations, the next may roll them back.
• Supply chain risk shifts with export rules. AI chips, telecom components, and security tooling all ride the geopolitical conveyor belt.

Here’s what I’ve found useful as a mindset: treat geopolitics like weather. You can’t control it, you can’t wait it out, and it doesn’t excuse being unprepared. You build for it.

Why sanctions rarely stop intrusions

Answer first: Sanctions can raise costs and constrain travel or financial access, but they don’t reliably stop espionage campaigns—especially those aligned to national objectives.

Nation-state programs are designed to be persistent. They use contractor ecosystems, layered infrastructure, and long timelines. Even if sanctions hit individuals or front organizations, the campaign often continues under a different set of names and pipes.

That’s why the article’s “deterrence by denial” framing matters: the only deterrence you control is making intrusion expensive and short-lived.

Salt Typhoon as a blueprint for “policy-lag” risk

Answer first: Salt Typhoon illustrates the gap between public responses (sanctions, statements, regulations) and operational reality (lateral movement, persistence, data access)—and AI can help compress the defender’s timeline.

Public reporting cited in the original piece describes Salt Typhoon’s growth from a targeted telecom operation to a much wider campaign affecting 200+ companies across 80 countries. Telecom intrusions are uniquely dangerous because telecoms sit at the crossroads of identity, communications metadata, lawful intercept surfaces, roaming relationships, and enterprise connectivity.

This is exactly the kind of environment where policy-lag becomes obvious:

• Diplomatic actions happen in weeks or months.
• Regulations move in quarters or years.
• Intrusion activity happens in minutes.

If you’re defending critical infrastructure, federal suppliers, telecom-adjacent enterprises, or any organization that routes sensitive communications, the practical question is: How do we reduce dwell time and blast radius even when the macro environment is unstable?

AI isn’t magic, but it does one thing extremely well when deployed correctly: it moves detection and triage from human-scale to machine-scale.

The operational gap AI can actually close

Answer first: AI is most effective when it automates the boring, high-volume work—correlating signals, prioritizing incidents, and accelerating containment—so humans can focus on judgment calls.

In large compromises, defenders don’t fail because they lack tools. They fail because:

• Alerts arrive faster than analysts can triage.
• Signals are spread across endpoints, identity, email, cloud logs, and network telemetry.
• Attackers move laterally before consensus forms.

Well-designed AI pipelines help by:

1. Entity correlation: joining identity events, endpoint behavior, and network paths into a single story.
2. Anomaly detection: spotting “low and slow” behaviors that signature rules miss.
3. Decision support: summarizing evidence and suggesting playbooks with confidence scoring.
4. Automation: executing containment steps (with guardrails) quickly.

That’s how you make “deterrence by denial” real: reduce attacker time-on-keyboard and increase the chance you catch them during staging, not exfiltration.

What “deterrence by denial” looks like in 2026 operations

Answer first: Deterrence by denial is a measurable program: harden common entry points, instrument everything important, and respond fast enough that persistence becomes uneconomical.

The source article highlights a truth many teams resist: you can’t “sanction your way” out of compromises, especially supply chain compromise. So what does denial look like at the enterprise or agency level—particularly heading into 2026 budgets?

Denial is built on three measurable loops

Answer first: If you can’t measure these loops, you can’t claim denial: prevention coverage, detection speed, and containment reliability.

1. Prevention coverage (hardening):

   • MFA-resistant authentication (phishing-resistant methods for privileged access)
   • Patch SLAs for internet-facing systems
   • Egress controls for high-risk protocols

2. Detection speed (instrumentation):

   • Centralized identity telemetry (SSO, conditional access, PAM)
   • Endpoint sensors with memory/process visibility
   • Network and DNS telemetry for command-and-control patterns

3. Containment reliability (response):

   • Tested isolation actions for endpoints and accounts
   • Segmentation that actually limits lateral movement
   • Backup and recovery drills that assume an active adversary

AI fits primarily into loops 2 and 3. It’s not there to “replace” controls; it’s there to keep the loops tight at scale.

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

Answer first: AI is strongest at correlation and prioritization; it’s weakest when organizations expect it to compensate for missing telemetry or weak identity controls.

AI can’t infer what you don’t log. It can’t “reason” its way around blind spots. If your environment lacks:

• consistent identity logging,
• endpoint coverage on high-value systems,
• cloud audit trails,

…your AI layer will mostly produce confident-sounding noise.

A better approach is to treat AI as an acceleration layer on top of a disciplined security architecture.

A practical AI security blueprint for policy uncertainty

Answer first: Build an AI-driven security program around a short list of “must-win” use cases: identity abuse, lateral movement, data access, and supplier risk.

If your board asks, “Are we exposed if national policy shifts?” the honest answer is, “Policy won’t stop intrusions.” The reassuring answer is, “We can still reduce impact.” Here’s a blueprint I’d recommend for 2026 planning.

1) AI-driven identity threat detection

Answer first: Identity is the control plane; AI should prioritize identity anomalies that lead to privilege escalation and persistence.

Focus on:

• Impossible travel and atypical device sign-ins (with context, not just distance)
• Token abuse patterns (sudden token refresh spikes, unusual client apps)
• Privilege changes (PAM checkout anomalies, role grants outside norms)

What “good” looks like: automated alert grouping by user + device + session, with a plain-language narrative your analyst can validate quickly.

2) AI for telecom-adjacent network visibility

Answer first: Telecom campaigns often blend normal traffic with targeted persistence, so AI should model baselines and flag deviations tied to high-value routes.

If you’re a telecom, an ISP, a managed network provider, or a large enterprise with heavy carrier dependencies:

• baseline DNS and egress patterns for critical enclaves
• apply anomaly detection to management-plane access
• correlate configuration changes with identity actions

This is less about “finding malware” and more about finding administrative misuse and stealthy tunnels.

3) AI-assisted incident triage and response

Answer first: The fastest wins come from AI that reduces triage time and enforces consistent containment actions.

Practical capabilities to prioritize:

• automatic deduplication of alerts into incidents
• evidence summaries (what happened, where, which accounts)
• recommended playbooks with human approval gates

A small but meaningful metric: track time-to-triage and time-to-containment per incident category. If AI doesn’t measurably reduce these, it’s theater.

4) Supply chain and contractor posture analytics

Answer first: If sanctions and export rules fluctuate, supplier risk becomes dynamic—AI can help track exposure faster than spreadsheets.

Use AI to:

• map software dependencies and flag risky components
• detect unusual update behavior (timing, signer changes, repo anomalies)
• prioritize third-party access reviews based on actual access patterns

This aligns with the “defense & national security” theme: mission assurance depends on the weakest supplier connection, not your best internal control.

What leaders should do before Q1 budgets lock

Answer first: Treat geopolitics as an input, not a strategy—then fund measurable denial capabilities where AI improves speed and coverage.

December is when many teams finalize 2026 plans. If you’re in that cycle now, I’d push these actions:

1. Pick two AI use cases that reduce response time within 90 days. (Identity triage and incident summarization are common winners.)
2. Define three metrics the business will recognize:
   • mean time to detect (MTTD)
   • mean time to contain (MTTC)
   • percentage of incidents with complete identity + endpoint + network evidence
3. Run an intrusion simulation that assumes policy doesn’t help. Make the scenario realistic: token theft, telecom-style persistence, and cross-tenant cloud access.
4. Put human guardrails around automation. AI-assisted containment should require approvals at first, then graduate to automation where risk is low and confidence is high.

A useful one-liner for the exec team: “Sanctions are a signal; security is a system.”

The real question: are you building for statements—or for intrusions?

Trade negotiations, export rules, and sanctions will keep swinging. That’s normal. The mistake is building a security posture that assumes they’ll swing in your favor.

Salt Typhoon is a reminder that cyber risk is now part of statecraft, and statecraft is inherently transactional. Your best defense is denial: better visibility, faster response, tighter identity control, and fewer places for attackers to hide. AI-driven cybersecurity is a practical way to get there because it scales the defender’s attention when adversaries scale their operations.

If you’re responsible for a security program in critical infrastructure, a federal supply chain, or any mission-essential enterprise, the next planning step is simple: choose where AI will measurably reduce attacker dwell time in 2026—and fund that first.

Where are you most exposed right now: identity, telecom-connected networks, or third-party access?