AI vs Disinformation: Defending the Human Algorithm

False news doesn’t “win” because it’s smarter. It wins because it’s better matched to how humans share.

A large-scale analysis of social sharing patterns found that false stories reached their first 1,500 people about six times faster than true stories—and the main driver wasn’t bots. It was us: the impulse to pass along the novel, the outrageous, and the identity-affirming.

For leaders working in defense, intelligence, public safety, and critical infrastructure, that fact lands differently. Disinformation isn’t just a communications headache. It’s an operational threat—one that can degrade readiness, inflame unrest, misdirect resources, and erode trust in institutions. In the “AI in Defense & National Security” series, this is the part we can’t afford to treat as a side quest: cognitive warfare is now a frontline domain, and AI has to be part of the counter.

Disinformation outruns truth because it rides human psychology

Disinformation spreads faster because it’s engineered for attention, not accuracy. That’s the core insight behind what former intelligence leaders and behavioral scientists alike describe as the “human algorithm”—predictable cognitive patterns that influence what we click, believe, and share.

The mechanisms are straightforward:

Novelty beats familiarity

People share what feels new. A mundane correction rarely outcompetes a startling claim, even when the correction is true. Novelty is a built-in prioritization signal in the brain, and adversaries have learned to manufacture it at scale.

In practice, this means disinformation operators don’t need perfect lies. They need fresh angles—a new “leak,” a new “insider screenshot,” a new “breaking” allegation that triggers curiosity.

Emotion beats deliberation

Strong emotion compresses decision time. Outrage, fear, disgust, and shock create a sense that something must be forwarded now. That urgency is exactly what verification requires you to resist.

The security implication is blunt: time-to-share is often shorter than time-to-check. So a single emotionally loaded post can achieve mission effect before any official response is even staffed.

Identity beats evidence

People also share to signal belonging—political identity, professional identity, community identity, even “I’m the kind of person who sees what others don’t.” Disinformation that flatters an in-group or attacks an out-group is sticky because it’s not just information; it’s social positioning.

This is why influence campaigns often aim less at belief and more at tribal sorting: pushing audiences into hardened camps that interpret every new claim through loyalty rather than proof.

Operational reality: Modern disinformation doesn’t always try to convince you of one story. It often tries to make you unsure whether truth is even knowable.

AI amplifies cognitive warfare—volume, realism, and targeting

AI doesn’t invent manipulation, but it accelerates it in three ways that matter to national security: production, personalization, and plausibility.

1) Production at industrial scale

Synthetic text and image generation allows small teams to create thousands of posts, comments, memes, and fake “local news” snippets quickly. The effect isn’t just reach—it’s information saturation, where analysts, journalists, and government communicators are forced into a reactive posture.

2) Personalization and micro-targeting

Influence operators increasingly tailor narratives to specific communities: veterans, parents, diaspora groups, regional audiences, or employees in a particular industry. AI makes it cheaper to maintain many “versions of the same lie,” each tuned to local language and values.

For defense organizations, this hits close to home:

• A narrative aimed at service members might focus on trust in leadership and benefits
• A narrative aimed at defense suppliers might focus on procurement “corruption” rumors
• A narrative aimed at local communities near bases might focus on safety incidents or environmental claims

3) Plausibility through synthetic media

Deepfake video, synthetic personas, and fabricated “evidence” change the burden of proof. When realistic artifacts are easy to generate, verification gets slower and more expensive.

The most damaging moment is often the first 60 minutes—when a synthetic clip spreads, gets mirrored, and becomes “common knowledge” before any authoritative debunk can catch up.

What “AI defense” looks like: from content moderation to mission capability

AI-driven counter-disinformation can’t be framed as a single tool or a social media policy. In defense and national security, it needs to be treated as a capability stack—integrated into intelligence analysis, cyber defense, operational planning, and crisis communications.

Here’s the model I’ve found most useful when advising teams: Detect → Attribute → Assess → Respond → Learn.

Detect: Find campaigns early, not viral

The goal is early-warning, not perfect classification.

Effective detection uses AI to identify signals such as:

• Coordinated posting patterns across accounts (timing, phrasing, reuse)
• Narrative clustering (many variants of the same claim)
• Sudden shifts in sentiment around a defense topic (base incidents, deployments, elections)
• Cross-platform propagation (a rumor moving from fringe channels into mainstream feeds)

If your system only triggers once something is trending, you’re already behind. Defense-grade detection prioritizes weak signals and “pre-viral” anomalies.

Attribute: Separate noise from adversary activity

Attribution isn’t always about naming a state actor publicly. Often it’s about internal confidence: “Is this organic outrage, opportunistic grifting, or coordinated influence?”

AI helps by correlating:

• Infrastructure indicators (domain reuse, hosting patterns)
• Persona behavior (linguistic fingerprints, account lifecycles)
• Content reuse networks (memes and templates appearing across communities)

This is where fusion matters: pairing AI content analysis with cyber threat intelligence and HUMINT/OSINT context to avoid chasing ghosts.

Assess: Translate narratives into risk

Most organizations get stuck arguing whether something is true or false and miss the bigger question: What’s the operational impact if this spreads?

Risk assessment should score:

• Audience proximity: Is it reaching decision-makers, operators, or vulnerable communities?
• Actionability: Does it encourage real-world behavior (protests, harassment, insider threats)?
• Timing: Is it aligned to an exercise, election window, procurement decision, or crisis?
• Trust damage: Does it target institutions, chain of command, or emergency services?

Think of this as “narrative triage.” Not every falsehood deserves the same response.

Respond: Increase truth velocity without sounding like a press release

The most effective counter isn’t louder messaging—it’s faster, clearer, and more human messaging delivered through trusted channels.

A strong response playbook includes:

1. Pre-bunking (before the lie lands): “Here’s what to expect, here’s how we verify.”
2. Rapid clarification (first hour): simple facts, confirmed unknowns, next update time.
3. Localized messengers: commanders, community leaders, agency SMEs, not just central HQ.
4. Receipts-ready communication: visuals, timelines, and plain language that travel well.

AI can support this by drafting initial holding statements, generating audience-specific FAQs, and recommending which claims to address based on spread velocity and harm.

Learn: Treat disinformation as an adversary adapting to your defenses

After-action reviews shouldn’t end with “we posted a correction.” They should capture:

• Which narratives broke through and why
• Which communities were targeted
• Which channels were slow or untrusted
• How long it took to issue a credible update

Then retrain models and update playbooks. If you don’t learn, the adversary does.

“Mind sovereignty” is a national security control, not self-help

One of the most underappreciated points in modern influence operations is that human attention is part of the attack surface.

The individual practice of pausing before sharing—especially when content is emotionally spiky—scales into an organizational advantage. You can treat this like a soft cultural issue, or you can treat it like what it is: risk management for cognitive security.

Two interventions consistently work in real teams because they fit how people actually behave:

1) Accuracy prompts at the moment of sharing

A simple “Do you think this is accurate?” prompt reduces resharing of questionable content. That’s not magic; it’s friction. It forces a brief switch from emotional reaction to reflective judgment.

For defense environments, this can be implemented as:

• A lightweight prompt inside internal collaboration tools
• A browser extension or secure gateway nudge on risky domains
• A short “verify before share” banner in internal comms channels during crises

2) Narrative literacy training for operators and leadership

Annual compliance training won’t cut it. Effective training looks like mission rehearsal:

• Show real examples of influence narratives aimed at military and public institutions
• Teach “pattern recognition” (novelty, outrage, identity hooks)
• Practice rapid verification workflows
• Clarify escalation paths: who to notify, what evidence to capture, what not to amplify

The win condition is simple: reduce impulsive amplification and increase reporting of suspicious narratives.

A practical blueprint: building an AI counter-disinformation capability

Defense and national security organizations that want this done right usually need three layers: technology, process, and governance.

Technology layer (what to build or buy)

• Narrative detection and clustering (multilingual, cross-platform)
• Synthetic media forensics (image/video provenance, tamper signals)
• Threat intel integration (correlate narrative activity with cyber/counterintelligence signals)
• Analyst copilots (summarize campaigns, generate timelines, propose hypotheses)

Process layer (how it runs day-to-day)

• A standing “narrative watch” function (like a SOC, but for influence)
• Clear severity levels and response SLAs (15 minutes, 1 hour, 24 hours)
• Pre-approved response templates and spokesperson rosters
• Red-team exercises for information operations and deepfakes

Governance layer (how you keep it legitimate)

This matters because counter-disinformation can drift into overreach if you don’t set boundaries.

• Define what you monitor (threat narratives) vs. what you don’t (protected speech)
• Maintain audit logs for model decisions and analyst actions
• Separate intelligence assessment from public messaging approval
• Use privacy-preserving methods where possible, especially for internal telemetry

Done correctly, AI supports democratic resilience rather than undermining it.

Where this fits in “AI in Defense & National Security”

AI is already central to surveillance, cyber defense, and intelligence analysis. Countering disinformation is the connective tissue across all three, because cognitive warfare targets the thing those systems serve: human decision-making.

If your AI roadmap includes autonomous systems, mission planning, and threat detection but ignores influence operations, you’re protecting hardware while leaving the operator’s mind exposed.

Truth can’t defend itself at the speed modern platforms move. That’s why the near-term goal isn’t perfect persuasion; it’s operational resilience—the ability to detect narrative attacks early, assess their impact, and respond fast enough that reality stays usable.

If you’re building or modernizing a counter-disinformation program—whether for defense, government, or critical infrastructure—now’s a good time to pressure-test your stack. How quickly can you detect a coordinated narrative? How fast can you produce a credible update? And what happens when the next “evidence” drop is a deepfake timed to a crisis?

The question isn’t whether disinformation will target your organization. It’s whether your response will be manual and late—or AI-assisted and ready.