Ransomware Payments Hit $4.5B—AI Stops Them Earlier

$4.5 billion. That’s how much ransomware payments have been tracked by the US Treasury’s Financial Crimes Enforcement Network (FinCEN) since 2013—and the uncomfortable part is how fast the curve bent upward.

FinCEN data shows $2.1B in reported payments from 2022–2024 across 4,194 incidents (from 7,395 related filings). Compare that with about $2.4B from 2013–2021. Three years roughly matched the prior nine. If you’re running security or risk, this isn’t just a “crime trend.” It’s a sign your business is operating inside a ransomware economy that’s gotten efficient.

This post is part of our AI in Cybersecurity series, and I’ll take a clear stance: the best time to beat ransomware is before encryption—and AI is most useful precisely in that pre-encryption window. Not as magic. As a force multiplier that spots weak signals, shrinks attacker dwell time, and helps your team act faster than the extortion playbook.

What the Treasury numbers actually tell you (and what they don’t)

FinCEN’s dataset is valuable because it’s grounded in financial reporting tied to anti-money laundering obligations. It’s also incomplete by design: it reflects what was reported under Bank Secrecy Act (BSA) coverage, not every ransomware event in the wild.

Here’s what is clear and extractable for leaders:

• Scale: $4.5B in tracked payments (2013–2024)
• Acceleration: $2.1B reported in 2022–2024 alone
• Peak year signal: 2023 was the high-water mark in the reporting window, with roughly $1.1B in payments and a 77% increase over 2022
• Payment rails: Bitcoin dominated (thousands of payments totaling roughly $2B), with smaller-but-notable usage of privacy coins like Monero (tens of millions)

The “smaller ransoms, more victims” shift matters

The data and expert commentary point toward a trend many defenders are feeling: ransomware operators are optimizing for volume. Instead of a few huge whale hunts, more groups go after mid-market and smaller orgs that are easier to breach, easier to pressure, and less prepared.

That changes the operational reality:

• Your org might not be “high profile,” but you can be highly profitable.
• Ransomware is now a process business: initial access, privilege escalation, exfiltration, extortion, repeat.
• You’re not just defending endpoints—you’re defending time.

Ransomware is a race between attacker automation and your detection-and-response speed. AI helps when it reduces the time it takes to notice what matters.

Why ransomware spiked: it’s not only about malware

Ransomware didn’t become lucrative just because encryption works. It became lucrative because the operating model matured.

RaaS and specialization lowered the bar

Ransomware-as-a-service (RaaS) made cyber extortion modular. One crew writes malware, another buys credentials, another negotiates. That specialization creates throughput.

Double extortion made “backup strategy” necessary but insufficient

Once data theft became standard, the attacker’s leverage moved from “we encrypted you” to “we’ll publish your data.” Good backups are still non-negotiable, but they don’t neutralize reputational, legal, or regulatory pressure.

Initial access shifted to the easiest doors

Operators increasingly target:

• Under-secured edge devices (VPNs, remote access gateways)
• Weak identity posture (stolen credentials, MFA fatigue, legacy MFA)
• Unmanaged SaaS sprawl
• Over-privileged accounts and service accounts

The takeaway: ransomware is often the final act. The real story is identity, exposure management, and response speed.

Where AI helps most against ransomware (practically, not hypothetically)

AI is most valuable in ransomware defense when it’s used to detect patterns of preparation, not just the encryption event.

1) AI-driven anomaly detection for pre-encryption behaviors

The “ransomware moment” is preceded by observable moves:

• Unusual authentication patterns (impossible travel, new device fingerprints)
• Privilege escalation attempts and sudden group membership changes
• Atypical remote admin tool usage (PowerShell bursts, PsExec, WMI)
• Large-scale file access, staging, and compression prior to exfiltration
• New outbound connections to rare destinations, especially from servers that shouldn’t talk to the internet

Rule-based detection catches some of this, but it’s brittle and noisy. AI-based behavioral analytics can surface combinations that look normal individually but suspicious together.

A snippet-worthy way I explain it internally is:

Ransomware prevention isn’t about catching the payload; it’s about catching the rehearsal.

2) AI to shrink alert fatigue and increase action rate

Most SOCs don’t fail because they “lack alerts.” They fail because they lack clarity.

Good AI workflows can:

• Correlate low-severity alerts into a single incident narrative
• Prioritize by blast radius (domain admin involved, critical server touched)
• Summarize activity in plain language for faster triage
• Recommend containment steps based on the observed kill chain

If you’re evaluating tools, don’t get distracted by shiny dashboards. Ask one question: Does this reduce the time from first signal to containment?

3) AI-assisted incident response that focuses on containment first

When ransomware is in motion, your team needs fast, consistent steps:

1. Isolate affected hosts and stop lateral movement
2. Disable compromised identities and rotate credentials
3. Block known malicious egress and command-and-control patterns
4. Preserve evidence for forensics and insurance/legal requirements

AI copilots can help by generating checklists, drafting internal comms, and summarizing scope—but they should not replace hard controls and practiced runbooks.

My opinion: AI is a great co-pilot during chaos, but only if your organization already agreed on who’s allowed to push the “isolate” button.

The money trail is part of defense: payments, compliance, and risk

The Treasury angle matters because ransomware is also a financial crime workflow. Whether you pay or don’t, ransomware drags in finance, legal, insurance, and executive leadership.

“Should we pay?” is the wrong first question

The better first questions are:

• Can we restore core operations without paying? (RTO/RPO reality, not theory)
• What data was exfiltrated and how sensitive is it?
• Is the attacker tied to sanctioned entities? (legal exposure)
• What’s the negotiation and verification plan if we engage at all?

Even when organizations don’t pay, they incur big costs in downtime, recovery labor, third-party IR, customer notifications, and reputational impact.

A useful KPI: probability-weighted loss

If you’re trying to get budget approval, don’t lead with fear. Lead with math.

A simple model security leaders can present:

• Annual probability of ransomware disruption × estimated total cost of disruption
• Compare that to the cost of improving detection + response speed

AI security investments win when they can demonstrate they reduce either:

• The probability of successful intrusion or
• The impact window (time to detect/contain) or
• The scope (blast radius)

A “winter readiness” ransomware checklist for 2026 planning

December is when a lot of teams are setting Q1 priorities, and attackers know holiday staffing gaps are real. If you want practical next steps that map to the FinCEN trendline, here’s what I’d prioritize.

Baseline controls that still beat most ransomware

• Patch edge-facing systems aggressively (VPNs, gateways, remote management)
• Enforce phishing-resistant MFA for privileged access and remote access
• Maintain cold backups (offline/immutable) and test restores quarterly
• Segment critical systems and restrict east-west traffic
• Reduce admin sprawl: just-in-time access, fewer standing privileges

Where to add AI for maximum ROI

• Identity threat detection: anomalous sign-ins, token abuse, privilege changes
• Behavioral analytics: lateral movement, rare process chains, data staging
• Email and collaboration security: BEC, credential phishing, malicious attachments
• SOAR + AI triage: faster correlation, less noise, consistent response actions

Minimum viable “stop the bleeding” playbook

Write it down, practice it, and make it executable at 2 a.m.:

1. Who declares an incident, and who can isolate systems?
2. What gets shut off first: VPN, RDP, identity sessions, EDR network containment?
3. What’s your known-good restore path for Tier-0 services (identity, DNS, core SaaS)?
4. Which executives are notified, and who talks to customers?

If AI is part of your SOC stack, include it in tabletop exercises. Tools you don’t practice with don’t save you.

People also ask (and the straight answers)

Does AI stop ransomware by itself?

No. AI improves detection and response speed, but ransomware resilience still depends on identity hardening, segmentation, backup recovery, and practiced incident response.

Why do ransomware payments rise even when arrests happen?

Because the ecosystem is distributed. When a major group is disrupted, affiliates and splinter crews often regroup, rebrand, and keep operating. Pressure helps, but it doesn’t remove the economics.

If payments are reportedly dropping in 2024–2025, should we relax?

No. A dip in payments can reflect better refusal-to-pay posture or law enforcement disruption, but attackers can compensate through higher volume and more efficient intrusions. Your risk doesn’t go away; it changes shape.

What I’d do next if I owned security outcomes

FinCEN’s $4.5B number is a scoreboard for attackers, but it’s also a planning tool for defenders. It tells you ransomware is not random—it’s repeatable, monetized, and tuned for speed.

If you want a defensible, budget-friendly strategy for 2026, anchor it on one objective: reduce time-to-containment. That’s where AI in cybersecurity earns its keep, especially when paired with fundamentals like phishing-resistant MFA, hardened perimeter devices, and restores you’ve actually tested.

If your team had to stop an attack at the exfiltration stage—not the encryption stage—would you be confident in what alerts you’d see, who would act, and how fast you’d contain it?