AI vs. Ransomware: Stop Payments Before They Start

The US Treasury has now tracked $4.5 billion in ransomware payments since 2013. That number should land like a budget alarm, because it isn’t abstract “cyber risk.” It’s real money leaving real businesses—often at the worst possible moment, when operations are down, customers are waiting, and leadership is under pressure to make a fast decision.

Even more telling: $2.1B of that total happened in just three years (2022–2024) based on Bank Secrecy Act (BSA) reporting—7,395 reports tied to 4,194 incidents. It’s not just that ransomware is common. It’s that the extortion economy has matured into a repeatable business model.

This post is part of our AI in Cybersecurity series, and I’m going to take a clear stance: if you’re still relying mainly on human-speed detection and manual triage, you’re building a ransomware program around hope. AI-driven threat detection and response won’t “solve” ransomware by itself, but it can shrink the attacker’s window so dramatically that paying a ransom becomes far less likely.

What the Treasury data reveals about ransomware’s business model

The headline isn’t only “$4.5B.” The bigger message is how ransomware changed shape—and why defenses need to change with it.

FinCEN’s dataset shows ransomware payments accelerated sharply in the last few years:

• 2013–2021: about $2.4B across 3,075 BSA reports
• 2022–2024: more than $2.1B across 7,395 BSA reports tied to 4,194 incidents
• 2023 peak: about $1.1B in payments (a 77% increase over 2022)

The report also highlights patterns defenders should treat as operational priorities:

• Most targeted industries: financial services, manufacturing, healthcare
• Most prominent group (2022–2024): Alphv/BlackCat
• Primary payment method: Bitcoin (roughly $2B across 3,489 payments)
• Monero is present, but smaller: $25.8M across 55 payments

Why 2023 spiked—and why that matters operationally

The spike around 2023 aligns with what many incident responders saw: large RaaS players operating at high tempo, plus a broader affiliate ecosystem that scaled down-market. After law enforcement disruptions (notably in 2024), the ecosystem didn’t disappear—it fragmented. Fragmentation often means:

• more groups,
• more experimentation,
• more “spray and pressure” targeting of mid-market organizations.

That’s a key point: ransomware isn’t only a Fortune 500 problem anymore. The economics now support high-volume extortion against smaller firms, because tooling, initial access brokerage, and automation make each intrusion cheaper.

Why “paying less often” isn’t the same as “being safer”

There’s encouraging data: one incident response firm reported ransom payment rates fell to 23% recently, and average payments dropped sharply in 2025. That’s progress.

But it would be a mistake to read that as “ransomware is fading.” What I’ve found is that organizations often confuse fewer payments with fewer compromises. In reality, several things can be true at once:

• Attackers can demand smaller ransoms and still be profitable.
• Victims can refuse payment but still suffer days of downtime and regulatory exposure.
• Data theft (double extortion) can deliver lasting harm even without encryption.

A useful framing for leadership is this:

Ransomware cost is a blend of ransom + downtime + recovery + legal + reputation. Reducing payments helps, but reducing blast radius is what changes the business outcome.

That’s exactly where AI in cybersecurity earns its keep.

Where AI actually helps against ransomware (and where it doesn’t)

AI works best in ransomware defense when it’s used for speed, correlation, and prioritization—not as a magical black box.

1) Early detection: catching the intrusion before the “big moment”

Most ransomware incidents aren’t a single event. They’re a chain:

1. Initial access (phishing, stolen credentials, exposed VPN/appliance, initial access broker)
2. Privilege escalation and credential dumping
3. Lateral movement
4. Data discovery + exfiltration
5. Encryption and extortion

AI-driven threat detection is valuable because it can flag weak signals across that chain faster than a human can—especially in noisy environments.

Examples of AI-friendly detections that matter:

• Identity anomalies: impossible travel, unusual MFA resets, risky OAuth consent, atypical token usage
• Lateral movement indicators: new admin shares accessed at odd hours, remote execution patterns, sudden increase in RDP/SMB usage
• Data staging signals: abnormal archive creation, mass file reads, spikes in compression utilities
• Exfil anomalies: unusual outbound volume, rare destinations, new cloud storage endpoints

The goal isn’t perfect prediction. The goal is earlier certainty.

2) Faster triage: reducing “alert fatigue” where attackers hide

Ransomware crews win by living in the gap between:

• “we saw something odd,” and
• “we’re confident enough to isolate systems.”

AI helps collapse that gap by correlating events into a narrative a responder can act on. Good security AI should answer questions like:

• What’s the likely initial access path?
• Which host is patient zero?
• Which identities were used, and which are now risky?
• Is there evidence of data exfiltration?
• What’s the next most likely attacker action?

If your SOC is still stitching this together manually across disconnected tools, you’re paying a “time tax” every time an incident starts.

3) Automated response: making containment a default, not a debate

When ransomware hits, minutes matter. AI-assisted response can safely automate bounded actions:

• Isolate endpoints with active encryption-like behavior
• Disable or step-up authentication for suspicious accounts
• Quarantine mailboxes after credential phishing confirmation
• Block outbound connections to high-risk destinations
• Freeze high-risk tokens/sessions and force re-auth

Automation shouldn’t replace human control. It should enforce pre-approved playbooks so the first containment moves happen even when the on-call analyst is still opening the ticket.

Where AI doesn’t help (enough) on its own

AI won’t rescue you if your basics are broken. If backups aren’t viable, if you can’t patch internet-facing systems quickly, or if identity controls are weak, AI becomes a very expensive way to watch yourself get compromised.

The best results come when AI is paired with fundamentals:

• fast patching for perimeter devices
• phishing-resistant authentication
• least privilege and strong segmentation
• tested, offline (or immutable) backups
• practiced incident response

A practical AI-driven ransomware defense playbook (what to implement next)

If you’re trying to reduce ransomware payments—and the operational chaos that drives them—this is the order I’d prioritize.

Step 1: Put identity at the center of ransomware detection

Ransomware is often an identity story first.

Implement:

• Risk-based login detection (impossible travel, anomalous device, atypical geo)
• AI-assisted monitoring for MFA resets, helpdesk impersonation patterns, and token abuse
• Automated containment: suspend or step-up auth on identities showing takeover signals

Why it works: attackers can’t encrypt what they can’t reach. Identity controls shrink reach.

Step 2: Add behavior-based endpoint detections for encryption and staging

Look for patterns, not just hashes.

Implement:

• Behavioral ransomware detection (rapid file modifications, suspicious rename/write patterns)
• Detection of mass compression + staging utilities appearing unexpectedly
• Automated host isolation when encryption-like behavior triggers with high confidence

Why it works: it reduces dwell time at the exact moment damage accelerates.

Step 3: Train AI on your “normal” to spot exfil that bypasses rules

Rules catch known bad. Exfiltration often looks like “weird normal.”

Implement:

• Anomaly detection on outbound volume, destinations, and rare protocols
• Entity-based analytics (user/host baselines)
• Alerting tuned to data stores that matter (finance, patient data, IP)

Why it works: double extortion depends on getting data out. Catch that and you change the negotiation power dynamic.

Step 4: Build an AI-assisted incident workflow that ends with a decision

This is where many teams stall: alerts don’t become actions.

Implement:

• Auto-generated incident summaries: timeline, affected assets, suspected tools, confidence levels
• Recommended playbooks with “approve/deny” response steps
• Evidence packages for legal/compliance and cyber insurance requirements

Why it works: ransomware response is as much coordination as it is technical work.

Step 5: Measure the metrics that actually reduce ransom pressure

Track these quarterly:

• MTTD/MTTR (mean time to detect/respond)
• Percentage of incidents contained before lateral movement
• Percentage contained before exfiltration
• Backup restore success rate (measured, not assumed)
• Number of high-severity identity anomalies per 1,000 users (trend matters)

Board-level translation: “How often would we be forced into a payment discussion?”

People also ask: common ransomware + AI questions

Does AI stop ransomware automatically?

No. AI improves speed and decision quality, then automation executes pre-approved actions. You still need strong identity, patching, backups, and response planning.

If ransomware payments are declining, should we invest less?

No. Payment rates can fall while attempts and intrusions rise. Attackers also shift to smaller demands and higher volume. Your goal is to reduce business disruption, not just avoid paying.

Which industries should care most right now?

Financial services, manufacturing, and healthcare show up prominently in the Treasury’s reporting. Practically, any org with high uptime requirements, sensitive data, or complex vendor access is a frequent target.

The point of AI in ransomware defense: make extortion fail more often

The Treasury’s $4.5B figure is a scoreboard for attackers. But it’s also a planning document for defenders: ransomware scales when detection is slow, response is manual, and leadership is forced into decisions under outage pressure.

AI in cybersecurity changes that equation when it’s used to spot the intrusion earlier, connect the dots faster, and trigger containment actions reliably. That’s how you reduce not only ransom payments, but the operational chaos that makes paying feel like the only option.

If your 2026 security roadmap includes “reduce ransomware impact,” here’s the question I’d put in front of your team: Which parts of our detection and response still run at human speed—and what would it cost us if attackers move faster than we do?