AI for Information Warfare: Detect, Decide, Defend

Information warfare doesn’t “support” modern conflict anymore—it sets the conditions for it. Long before missiles fly or sanctions land, audiences are primed: trust is eroded, alliances are stressed, and leaders are cornered into decisions made under public pressure.

That’s why the Cipher Brief’s recent focus on “Information Warfare: The New Frontline” hits a nerve for defense and national security teams. The fight is less about a single viral lie and more about industrial-scale narrative operations that run 24/7 across platforms, languages, and communities.

Here’s my take: most organizations still treat disinformation like a PR incident. The reality is it behaves more like a hostile, adaptive cyber campaign—and it demands the same kind of detection, triage, and response discipline. The difference now is that AI in defense and national security can finally make this problem observable at operational scale—if you build the right workflows.

Information warfare is operational, not “online noise”

Information warfare works because it targets three things that militaries and governments depend on: legitimacy, cohesion, and tempo. If an adversary can slow decision cycles, split public opinion, or make a partner doubt intelligence sharing, they’ve achieved effects that look a lot like battlefield advantage.

The modern pattern is consistent across conflicts and crises:

• Narrative shaping before events (pre-positioning “explanations” for future actions)
• Flooding the zone during events (conflicting claims, doctored media, pseudo-experts)
• Exploitation after events (blame assignment, demands for withdrawal, sanctions pressure)

What’s changed is speed and scale. Generative AI lowers the cost of producing persuasive content in multiple languages and formats. The new normal is not a single deepfake; it’s thousands of coordinated assets—posts, videos, comments, “local” pages, and bot-amplified talking points—working in parallel.

The real target: decision-making under pressure

In national security, disinformation isn’t just about being believed. It’s about forcing leaders into bad options:

• Responding publicly before verification is complete
• Redirecting resources toward false threats
• Losing credibility with partners because messaging diverges
• Creating friction between civil and military leadership

A useful one-liner for internal briefings is this:

Information warfare is a campaign to control what leaders feel they must do next.

Why AI is becoming the “radar” for strategic narratives

If information warfare is operational, then AI’s role is straightforward: make the invisible visible. Humans are good at judgment but terrible at scanning millions of posts, images, videos, and cross-platform behavior in real time.

AI is now essential for three reasons:

1. Volume: Monitoring at scale requires automated collection, clustering, and prioritization.
2. Velocity: Narratives move faster than traditional comms and intel cycles.
3. Variation: The same story appears in different languages, memes, and “local” wrappers.

This is where the “AI in Defense & National Security” series has been heading all year: AI isn’t just about autonomy or surveillance—it’s about sensemaking.

What “good” looks like: detection, attribution support, and effects tracking

AI should not be deployed as a magic “truth machine.” Used well, it functions more like a layered sensor system:

• Detection: Identify emerging narratives early (before they peak)
• Attribution support: Surface coordination signals (timing, reuse, network patterns) that analysts can evaluate
• Effects tracking: Measure reach, adoption, and cross-community spread to inform response choices

In practice, a strong pipeline blends machine speed with analyst accountability:

1. Ingest multi-platform content streams
2. Normalize data (language, metadata, timestamps)
3. Cluster into narrative topics
4. Score for risk and coordination indicators
5. Route to analysts for validation and response recommendations

The AI toolkit for counter-disinformation operations

Teams often ask, “What AI techniques actually matter?” The short list below covers what I see working in real operations.

Narrative detection with NLP (and why keywords aren’t enough)

Keyword alerts are brittle. Adversaries swap terms, use sarcasm, or embed claims in images. Modern natural language processing (NLP) approaches can cluster content by meaning rather than exact wording.

Practical capabilities to look for:

• Topic modeling and semantic clustering to detect “new storylines”
• Multilingual embeddings to track the same narrative across languages
• Stance detection to distinguish reporting from promoting a claim

Actionable output should look like: “This narrative is spreading in three communities, with two new variants appearing in the last six hours.” Not just a pile of posts.

Synthetic media detection: useful, but not sufficient

Deepfake detection matters, but it’s only one slice. In 2025, influence operations rely heavily on cheapfakes (edited clips, miscaptioned videos, audio splices) because they’re easier to produce and harder to adjudicate quickly.

A balanced approach:

• Use AI to flag likely manipulation (visual artifacts, compression anomalies, voice fingerprints)
• Pair with provenance checks (original upload paths, known camera signatures when available)
• Build a rapid “is it new?” workflow: many viral items are old content repackaged

Operational truth: a “90% deepfake likelihood” score is not a decision. It’s a trigger for fast verification.

Network and behavior analytics: where coordination shows up

Attribution is hard. But coordination leaves patterns AI can spot:

• Many accounts posting the same claim within minutes
• Reuse of identical images with slight cropping or color changes
• Cross-platform “handoffs” (content seeded in one place, amplified elsewhere)
• Repeating URL infrastructure, redirect chains, or shared admin behavior

This is the overlap between AI for cybersecurity and AI for information operations. You’re hunting campaigns, not individual posts.

Risk scoring that leaders can actually use

If your dashboard is only for analysts, you’re missing the point. Leaders need an interpretable risk view:

• Probability of coordination (low/medium/high, with supporting signals)
• Projected reach (based on early velocity and amplifier accounts)
• Targeted audience (military families, allied publics, specific regions)
• Potential operational impact (election integrity, force protection, alliance trust)

The best systems also track counter-messaging outcomes (did the response reduce spread, or did it amplify the claim?).

A pragmatic operating model: how to run counter-information with AI

Tools don’t win this fight—workflows do. If you want AI-enabled situational awareness that holds up under scrutiny, build around four lanes.

1) Monitor like intel, not like marketing

Information warfare monitoring should sit close to intelligence and security operations, with clear thresholds and escalation paths.

Minimum viable monitoring stack:

• Multilingual collection and transcription (text, audio, video)
• Narrative clustering + trend detection
• Analyst review queue with audit trails
• Incident-style reporting templates (what, so what, now what)

2) Verify fast, then communicate with discipline

The biggest unforced error is responding too early. The second biggest is responding too late.

A response playbook that works:

1. Triage (is this gaining traction? who is it reaching?)
2. Verify (source, time, location, media integrity)
3. Decide response type (ignore, inoculate, rebut, disclose, disrupt)
4. Coordinate across agencies and allies to avoid contradictory statements

AI accelerates steps 1–2. Humans own steps 3–4.

3) Measure effects, not activity

Counting “number of posts” is a vanity metric. You care about:

• Narrative penetration into mainstream communities
• Adoption by credible voices (journalists, officials, influencers)
• Sentiment shifts inside target populations
• Policy pressure signals (calls to withdraw, sanction, retaliate)

This is where AI can support strategic decision-making: it shows whether a narrative is confined to the fringe or crossing into consequential audiences.

4) Build resilience: inoculation beats whack-a-mole

The most cost-effective counter-disinformation tactic is prebunking—explaining the techniques adversaries use before audiences encounter them.

AI helps identify which techniques are trending (fake experts, forged documents, “leaked” chats, manipulated video). Then communicators can inoculate specific communities with simple, credible guidance.

A strong line for internal alignment is:

You can’t fact-check your way out of a narrative campaign. You have to reduce its payoff.

Common questions leaders ask (and straight answers)

Can AI detect disinformation before it goes viral?

Yes—if you’re collecting early signals and clustering narratives in near-real time. The trick is separating “new story” from “new spike.” Trend velocity + amplifier detection is usually the earliest reliable warning.

Does AI solve attribution?

No. AI supports attribution by surfacing coordination indicators and infrastructure patterns, but attribution remains an analytic judgment that may require classified sources, legal review, and diplomatic considerations.

What’s the biggest implementation mistake?

Treating this as a software purchase. The winning move is an operational capability: trained analysts, clear escalation rules, and comms discipline—supported by AI.

Where this goes next for AI in defense and national security

The next step isn’t more dashboards. It’s tighter integration between information environment monitoring, cyber threat intelligence, and mission planning.

Over the next year, expect three shifts:

• Fusion cells that combine narrative telemetry with cyber and geopolitical indicators
• AI-assisted red teaming that simulates adversary narrative options before crises
• Provenance-by-default media practices (signing, watermarking, and chain-of-custody norms)

Information warfare is now a standing contest. If you’re only reacting to the last falsehood, you’re already behind.

If you’re building capabilities in this space, the fastest path to results is to start with one operational question: Which narratives could change a real-world decision in the next 72 hours—and how would we know?

If you want help answering that in your environment—data sources, model approach, and an operating model your leadership will trust—this is exactly the kind of AI-enabled national security work we focus on.