AI Spotting Russian Info Ops Before They Go Viral

A single “accidental” sighting at a Georgetown restaurant helped trigger days of headlines, speculation, and political heat in the U.S. The real trick wasn’t a forged document or a fake video. It was timing, plausibility, and audience psychology—the core ingredients of modern information operations.

Glenn Corn, a former CIA senior executive, described a telling episode from January 2018: senior Russian intelligence officials visited Washington for counterterrorism discussions under an agreement that there would be no public statements and no organized media coverage. Shortly after, news of the visit “leaked,” and the story mutated as it moved—picking up inaccuracies and insinuations that inflamed an already polarized environment.

This post uses that real-world case as a cautionary example in our AI in Defense & National Security series. The point isn’t nostalgia for a messy news cycle. The point is operational: adversaries don’t need to fabricate everything when they can nudge a system that will fabricate the rest. AI can help detect and counter that nudge—if agencies build the right workflows.

What this Russian information operation actually shows

The fastest way to understand the episode is this: the “payload” wasn’t a lie; it was a story designed to predictably produce division.

Corn recounts that the SVR director’s visit was coordinated and limited. Yet subsequent reporting included false claims (for example, that the head of Russia’s military intelligence traveled too) and insinuations of secret meetings. The result: a predictable wave of domestic suspicion and outrage.

Two practical lessons jump out for defense and intelligence teams.

1) “Truth-adjacent” stories scale better than obvious fakes

A story can be mostly true and still function as an information weapon. If it’s plausible, emotional, and ambiguous, it tends to:

• Travel faster than careful clarifications
• Invite confident speculation (“connect the dots”) from partisan corners
• Create durable suspicion even after corrections

This matters because many counter-disinformation programs are still overly focused on debunking falsities rather than detecting manipulative narrative dynamics.

2) Adversaries exploit your existing cracks, not their own strength

Corn’s framing is blunt: the Russians likely understood the political sensitivities in the U.S. at the time and used a low-effort leak to amplify distrust. Whether additional falsehoods were seeded or whether domestic actors did the distortion on their own, the mechanism is the same:

Information operations are most effective when the target audience does the “work” for the operator.

That’s a hard pill to swallow, but it’s operationally useful. It shifts the question from “How do we stop lies?” to “How do we stop predictable cascades?”

The anatomy of the operation: a simple playbook that still works in 2025

The reality? This pattern is still active—especially around elections, high-profile trials, geopolitical crises, and holiday-season attention peaks when rumor spreads faster than verification.

Here’s the playbook distilled into steps that show up again and again.

Step 1: Create a “leakable” moment with plausible deniability

A quiet dinner. A “chance” journalist sighting. A comment like “I can’t control what the press will write.” The operator doesn’t need to run a giant botnet to start a fire. They need a spark that looks organic.

Step 2: Rely on the target’s incentives to amplify

Media incentives reward speed. Political incentives reward insinuation. Social incentives reward outrage.

Once the spark hits a polarized environment, the story can quickly pick up:

• Extra characters (“the GRU chief was there too”)
• Hidden rooms (“secret White House meeting”)
• Motive laundering (“it must be a backchannel”)

Step 3: Let ambiguity do the damage

Ambiguity is sticky. A clean, sourced clarification rarely matches the emotional energy of a suggestive narrative. By the time official context arrives, attention has moved on.

Step 4: Bank the outcome: degraded trust

The strategic objective isn’t always to “convince.” Often it’s to:

• Degrade trust in institutions
• Increase cynicism (“everyone’s lying anyway”)
• Divide coalitions and publics
• Force leaders into reactive messaging

From a national security lens, that’s not “just politics.” It’s strategic terrain shaping.

Where AI helps—and where it absolutely doesn’t

AI won’t solve information warfare by “detecting fake news.” That framing is too narrow and too easy to evade.

AI does help when it’s used to detect patterns of manipulation and to speed up analyst decision cycles.

AI is strongest at pattern recognition across noisy information

Information operations create a messy signature: fragments across platforms, repeated phrases, coordinated timing, unusual account behavior, and narrative “handoffs” between communities.

AI can support analysts by:

• Clustering narratives across posts, outlets, and languages to reveal coordinated themes
• Flagging anomalous amplification (sudden velocity spikes, synchronized reposting, identical phrasing)
• Identifying cross-platform propagation paths (where a claim originated and how it jumped)
• Detecting semantic drift (how a factual seed morphs into insinuation and then “certainty”)

A practical way to say it: AI helps you see the shape of a story, not just the words in a story.

AI fails when it’s treated like an autonomous truth machine

If you deploy large language models as automated arbiters of truth, you’ll get burned. Reasons include:

• “Truth” often depends on classified context, not public text
• LLMs can hallucinate or overstate confidence
• Operators adapt quickly once they know what triggers filters

The right model is decision support, not decision replacement.

A defense-grade workflow for countering info ops (that doesn’t collapse under politics)

The biggest mistake agencies make is building a tool without building the workflow. Tools don’t defend democracies; disciplined operations do.

Here’s a workflow I’ve found holds up because it focuses on behaviors and effects, not partisan content.

1) Start with “narrative incident response,” not fact-checking

Treat viral narrative events like cyber incidents.

• Define severity levels (reach, velocity, targeting, proximity to sensitive events)
• Create a triage queue for narrative anomalies
• Set “time to context” objectives the same way you set “time to contain” in cybersecurity

Output: a short incident card—what’s spreading, where it started, and what it’s doing to trust and behavior.

2) Build a minimal, explainable AI stack

A practical stack for information operations detection often includes:

• Ingestion: social, broadcast transcripts, forum scrapes, internal reporting
• Embedding + clustering: grouping semantically similar claims
• Graph analytics: mapping accounts, reposts, outlet-to-outlet citations
• Anomaly detection: spotting unusual coordination and velocity
• Summarization with citations: generating analyst-ready briefs that point back to underlying evidence

The non-negotiable feature: auditability. If an analyst can’t explain why the model flagged something, it won’t survive real-world scrutiny.

3) Use “prebunking” playbooks for recurring tactics

Debunking is reactive. Prebunking is cheaper and faster.

Create short, reusable public-facing explainers for tactics like:

• “Accidental leak” narratives
• “Secret meeting” insinuations
• “Anonymous source” laundering
• “Two true facts + one false connector” stories

Then, when a narrative incident hits, you’re not writing from scratch under pressure.

4) Measure outcomes that matter: trust and behavior, not takedowns

Takedowns are sometimes necessary, but they’re not a strategy.

Metrics that actually help leadership decide:

• Narrative velocity reduction after context release (minutes/hours)
• Correction penetration across communities (who saw the context vs who saw the claim)
• Repeat-claim rate over 7 days (did it keep resurfacing?)
• Analyst cycle time (from detection to actionable brief)

If you can’t measure whether context reached the affected audience, you’re flying blind.

Common questions leadership asks (and straight answers)

“Can we just block the content?”

Not reliably. Operators thrive on martyr narratives (“they’re censoring us”). Blocking can also miss the point: the operation’s goal is often distrust, not distribution.

“Isn’t this just a communications problem?”

No. It’s an operational security problem because it can degrade alliance cohesion, force policy missteps, and distract decision-makers.

“Won’t AI create more false positives?”

Yes—if you treat flags as verdicts. The right approach is tiered confidence: AI flags, analysts validate, and leadership decides the response.

“What’s the fastest win we can get in 90 days?”

Stand up narrative incident response: one intake channel, one triage rubric, one dashboard for velocity and drift, and one cross-functional rapid context team.

What the 2018 episode should change in 2026 planning

This case study lands at a useful moment. December planning cycles are underway, budgets are tightening, and public attention is fragmented across platforms and private channels. That’s exactly when low-cost information operations pay off.

The episode Corn described should push defense and national security organizations toward a clearer stance:

• Information operations are not rare events. They’re background pressure.
• The most dangerous content is often plausible, not absurd.
• AI is necessary for scale, but human judgment is necessary for truth.

If you’re building an AI strategy for intelligence analysis and cybersecurity, don’t stop at “detect deepfakes.” Build the capability to detect narrative engineering—the quiet manipulation that turns ordinary reporting into institutional damage.

What would change in your organization if you treated viral political ambiguity with the same seriousness—and operational discipline—as a major cyber incident?