Ransomware Payments Hit $4.5B—AI Can Cut the Risk

FinCEN has now tracked $4.5 billion in ransomware payments since 2013. That number should change how you talk about ransomware internally: it’s not just a security problem, it’s a financial risk management problem—one that affects cash flow, insurance, credit exposure, and even board-level liability.

Here’s the part that should make every IT and finance leader sit up: the last three years (2022–2024) alone accounted for more than $2.1B across 4,194 reported incidents. That’s roughly the same scale as the prior nine years combined. Ransomware didn’t just grow—it found a repeatable business model.

This post is part of our AI in Cybersecurity series, and I’m going to take a stance: if your ransomware plan is still “patch faster and train users,” you’re underpowered. Those basics matter, but they don’t match the pace of modern ransomware operations. AI-driven security—used correctly, with guardrails—can materially reduce both the odds of an incident and the size of the loss when one lands.

What the $4.5B figure actually tells us (and what it doesn’t)

Answer first: The $4.5B total is a hard, government-tracked signal that ransomware is a mature criminal economy—but it’s also an undercount that still reveals key behavioral patterns defenders can use.

FinCEN’s dataset comes from reporting under the Bank Secrecy Act (BSA). That means it reflects activity that financial institutions and other covered entities observed and reported—often payments, attempted payments, or suspicious transaction patterns tied to ransomware.

The data is incomplete—yet still useful

It’s not a full census of ransomware.

• Many incidents never get reported through BSA channels.
• Some victims don’t pay (or pay in ways that don’t surface the same way).
• Some organizations may not realize a payment was ransomware-related if it’s masked as “consulting” or routed through intermediaries.

And still: 7,395 BSA reports tied to 4,194 incidents and $2.1B in payments (2022–2024) is enough volume to spot real trends.

The trend line is the story: 2023 spiked hard

FinCEN notes 2023 hit about $1.1B, a 77% increase over 2022. Other industry reporting aligns with a peak-and-cool pattern: Chainalysis has cited ~$813.55M in 2024, down about 35% from 2023.

A dip isn’t a victory lap. It often means one of three things:

1. Attackers adjusted pricing (smaller asks, more targets).
2. Enforcement disrupted major crews (and the ecosystem splintered).
3. Victims got better at refusal (and at restoring without paying).

All three appear to be true at once.

Why ransomware is thriving: volume beats “big game hunting”

Answer first: Ransomware has shifted toward efficient, repeatable intrusions—often against smaller organizations—because speed and scale produce steadier revenue.

The popular mental model is still “criminals go after Fortune 500s and demand eight figures.” That happens, but the more reliable model looks like e-commerce:

• More deals
• Lower price per deal
• Faster cycle time

Industry observers have noted a drift toward smaller ransom demands and shorter dwell times (less time spent expanding access before encrypting/exfiltrating). That’s consistent with a production-line operation.

How attackers made ransomware more predictable

Ransomware crews didn’t become “smarter” in a single leap. They optimized the pipeline:

• Initial access brokers sell entry to networks so ransomware teams can specialize.
• Exposed edge systems (VPNs, remote access appliances, web apps) remain high-yield.
• Double extortion (encrypt + steal + threaten leak) increased payment pressure.

And the RaaS model (ransomware-as-a-service) means the ecosystem can absorb disruption. When one brand collapses, affiliates migrate.

Here’s the uncomfortable truth I’ve seen play out: most organizations don’t get hit because they’re uniquely valuable. They get hit because they’re reachable.

The hidden cost: ransomware is a fraud and finance problem

Answer first: The ransom payment is often the smallest “line item” once you include downtime, recovery labor, legal exposure, and follow-on fraud.

FinCEN’s focus on payments makes it tempting to think in payment totals alone. But ransomware creates a stack of costs that frequently dwarf the transfer:

• Operational downtime (lost revenue, SLA penalties, canceled procedures in healthcare)
• Digital forensics + incident response (internal overtime plus external retainers)
• Data breach obligations (notifications, credit monitoring, regulatory counsel)
• Insurance friction (coverage disputes, higher premiums, more exclusions)
• Supply chain damage (partners pause integrations or require audits)

There’s also a fraud angle that’s easy to miss: once criminals are inside, ransomware becomes a distraction layer. While teams scramble to restore systems, threat actors (or parallel crews) may attempt:

• vendor payment diversion
• payroll changes
• account takeover and lateral movement into financial systems

That’s why I prefer framing ransomware as a blended risk: cyber extortion + data breach + financial fraud.

Where AI-driven security actually helps against ransomware

Answer first: AI helps most when it reduces time-to-detect and time-to-contain, correlates weak signals across tools, and blocks common ransomware precursors before encryption.

“Use AI” is meaningless advice. What works is using AI for the parts humans are worst at: speed, scale, correlation, and repetition.

1) Catching the early-stage behaviors attackers can’t avoid

Ransomware rarely begins with encryption. It begins with steps that leave traces:

• unusual authentication patterns (impossible travel, anomalous device posture)
• privilege escalation attempts
• mass discovery (enumerating shares, AD queries)
• disabling security tools or tampering with backups
• unusual SMB/RDP lateral movement

AI-based anomaly detection and behavioral analytics can flag these patterns earlier—especially when signals are spread across identity, endpoint, network, and cloud logs.

A practical benchmark I like: if your team only gets “high confidence” alerts after data is exfiltrated, your detection is too late.

2) Using AI in the SOC without creating chaos

Security teams fear AI because of alert storms and hallucinations. Fair concern. The way around it is to deploy AI where outputs are verifiable:

• alert clustering (group related events into one case)
• entity timelines (user/device storylines across logs)
• automated enrichment (asset criticality, known vulnerabilities, identity risk)
• guided triage with citations to underlying telemetry

The goal isn’t an “AI SOC” that replaces analysts. It’s an AI-assisted SOC that prevents analysts from spending 60% of their shift on copy/paste investigations.

3) Predicting exposure using historical patterns

This is where the FinCEN time horizon matters (2013–2025). Long-term ransomware data supports a smarter question than “Are we vulnerable?”

Ask: Which control failures predict our highest-loss scenarios?

AI can help model risk using your own history:

• patch lag on internet-facing services
• MFA coverage and exceptions
• recurring misconfigurations (cloud storage, remote admin tools)
• backup success rates and restore drill results
• incident response MTTR by business unit

That’s not sci-fi. It’s applying analytics to security operations the way finance applies analytics to working capital.

A pragmatic ransomware defense plan (AI + fundamentals)

Answer first: The strongest posture combines “boring controls” with AI-driven detection and response—because ransomware punishes gaps in both prevention and containment.

If you’re building a 2026-ready ransomware program, I’d structure it like this:

Prevention: reduce the number of doors

• Patch externally exposed systems on aggressive SLAs (days, not weeks)
• Enforce phishing-resistant MFA for admins and remote access
• Reduce standing privileges (just-in-time admin access, strong separation)
• Lock down remote management tools and scripts (signed, controlled)

Resilience: make paying unnecessary

• Maintain offline or immutable backups
• Test restores monthly for critical systems (not quarterly “someday” drills)
• Keep “gold image” rebuild paths for endpoints and servers
• Segment networks so one compromise doesn’t become total compromise

Detection & response: contain fast, with AI where it counts

• UEBA on identity and endpoints to catch lateral movement early
• AI-assisted case management to collapse noisy alerts into incidents
• SOAR playbooks for high-signal actions (disable account, isolate endpoint, block hash)
• Ransomware canary files / tripwires on high-value shares

If you want a single metric to rally leadership around, use this:

Your ransomware risk drops sharply when you can contain a foothold in under 30 minutes.

That containment speed is exactly where AI can pay for itself.

“Should we pay?” is the wrong first question

Answer first: The first question is whether you’re operationally forced to pay—and the only way to avoid that trap is preparation plus fast containment.

FinCEN’s numbers exist because payments keep happening. Some organizations pay because they can’t restore quickly enough, can’t tolerate leak risk, or can’t validate what was taken.

If you want to reduce the odds you’ll pay, build decision clarity before an incident:

• Define who can authorize payments and under what conditions
• Pre-negotiate IR and outside counsel relationships
• Establish a proof standard for “we can restore” (RTO/RPO that the business signs)
• Make sure you can answer “what data was touched?” with actual telemetry

AI-driven detection doesn’t replace that governance—but it makes the difference between a scare and a catastrophe.

What to do next (before the next invoice-sized ransom demand)

FinCEN’s $4.5B ransomware payment tally is a scoreboard, and it’s ugly. The encouraging sign is that payment rates and average payments have shown declines in some recent reporting—proof that defenders can squeeze the economics. But ransomware groups adapt quickly, and the “spray smaller orgs at scale” approach is tailor-made for 2026.

If you’re responsible for reducing financial exposure, start with two moves this quarter:

1. Run a ransomware tabletop that includes finance (wire approvals, insurance, disclosure, business continuity). Treat it like a liquidity drill.
2. Pilot AI-driven detection on identity + endpoint behavior with clear success criteria: time-to-detect, time-to-contain, and false positive rate.

The question worth ending on is simple: if an affiliate crew gets valid credentials tonight, do you know—fast enough to stop encryption—before Monday’s standup?