AI Security for Crypto Theft: Stop the Next $3.4B

$3.4 billion. That’s the amount stolen from the crypto industry in 2025 from January through early September, according to Chainalysis findings released on Dec. 18. And nearly half of that total was attributed to activity linked to North Korea.

If you run payments, fintech infrastructure, exchanges, stablecoin rails, or custody operations, this isn’t “crypto news.” It’s a stress test for the broader digital financial system. The techniques used in large crypto hacks—credential theft, social engineering, supply chain compromise, and laundering through fragmented venues—map painfully well to modern fraud in any real-time money movement stack.

Here’s my stance: security teams that rely on static rules and after-the-fact investigations are going to keep losing. The only approach that scales with adversaries (especially state-backed ones) is AI-driven security—not as a buzzword, but as a practical set of detection, decisioning, and response capabilities across wallets, APIs, and transaction flows.

Why $3.4B in crypto theft is a payments problem

Crypto theft at this scale is a clear signal: attackers have figured out how to move value faster than many institutions can detect and respond. That’s not a blockchain-only issue; it’s a real-time payments issue.

When stolen funds move, they don’t politely wait for your weekly fraud review meeting. They bounce across addresses, swap assets, bridge to other chains, and get broken into smaller parcels. Meanwhile, your compliance and security tooling often operates in silos: one team watches login anomalies, another watches withdrawals, another watches blockchain exposure, and none of them share a single risk picture.

The uncomfortable truth: speed beats control

Most fintech stacks were built for growth first. The result is common:

• Risk engines focused on chargebacks and card fraud patterns, not key compromise and rapid asset flight
• Security monitoring tuned for IT events, not financial behavior
• Manual review queues that are reasonable for low velocity, but collapse during coordinated attacks

Crypto just makes the failure mode visible. The same dynamic shows up in instant payments, RTP/SEPA Instant, wallet-to-wallet transfers, and API-based disbursements: when money movement becomes immediate, detection has to be immediate too.

What’s driving the spike: attacker playbooks are maturing

The Chainalysis summary points to a rise in hacks tied to North Korea. Whether you’re tracking attribution or not, what matters operationally is this: well-funded adversaries run repeatable playbooks. They learn. They automate. They target weak operational seams.

Where the money is really lost: control gaps, not cryptography

Large thefts typically don’t require “breaking the blockchain.” They exploit organizations:

• Compromised employee credentials (often via phishing, MFA fatigue, SIM swaps, or device takeover)
• API key leakage and poor secret hygiene
• Cloud misconfigurations (overly permissive roles, exposed admin panels)
• Weak segregation of duties (one identity can approve and execute high-risk actions)
• Vendor and software supply chain compromise
• Operational shortcuts in hot wallet management

If your organization has modern CI/CD, multiple third-party tools, remote work endpoints, and a growing list of integrations, you already have the same attack surface.

Laundering isn’t an afterthought anymore

Crypto laundering has become industrialized:

1. Rapid dispersion to many addresses (to reduce the value of any single freeze)
2. Asset swapping via DEXs and aggregators (to break simple tracing heuristics)
3. Cross-chain bridging (to exploit tooling gaps between chains)
4. Off-ramping through a mix of compliant and non-compliant venues

That chain of events is exactly why post-incident tracing alone doesn’t protect customers. It helps investigations, but it doesn’t reliably stop loss.

Where traditional controls fail (and what AI fixes)

AI in cybersecurity isn’t one product. It’s a set of capabilities that lets you detect patterns humans and rules won’t catch—fast enough to matter.

Static rules don’t handle adaptive fraud

Rules-based systems are easy to bypass once attackers learn the thresholds:

• “Withdrawals over X require review” becomes “withdraw X-1, repeatedly”
• “New device triggers step-up” becomes “compromise an existing device”
• “Block risky geographies” becomes “use residential proxies”

AI-based fraud detection works differently: it models behavior and context, then flags deviations that matter.

AI makes identity and transactions part of one risk story

A common fintech mistake is splitting identity security from transaction risk. Attackers love that.

An effective AI security posture correlates signals across:

• Identity: impossible travel, session hijack indicators, MFA resets, credential stuffing
• Device: emulator/root signals, fingerprint drift, remote access tooling
• Network: proxy anomalies, ASN reputation, TOR/VPN behavior patterns
• Transaction graph: destination novelty, velocity, hop patterns, clustering
• Wallet operations: admin actions, signing workflow anomalies, hot wallet exposure

The goal is a single answer to a single question: “Is this action consistent with legitimate behavior for this user, this device, and this moment?”

A practical AI security architecture for crypto and fintech rails

If you’re responsible for payments and fintech infrastructure, the “what do we do Monday?” question matters more than abstract strategy.

Here’s a blueprint I’ve seen work in high-velocity environments.

1) Detect: build real-time anomaly detection across money movement

Answer first: You reduce theft by detecting abnormal actions in milliseconds, not hours.

Deploy machine learning models that score risk for events like:

• New beneficiary setup
• API key creation/rotation
• Withdrawal address changes
• Large withdrawals after dormant periods
• Unusual transaction chaining (many small sends, rapid swaps)

Use two complementary model types:

• Behavioral models (baseline per user/entity and detect deviation)
• Graph models (identify suspicious clusters, hop patterns, and exposure to known bad infrastructure)

If you only do one thing, do this: move from “transaction rules” to “behavioral baselines + anomaly scoring.”

Operational tip: don’t chase “perfect” data

Security teams often stall because the data isn’t clean. Start with what you already have:

• Auth logs
• API gateway logs
• Wallet service events
• Ledger/transaction events
• Customer support signals (password resets, account recovery attempts)

AI improves with iteration. Waiting for a pristine dataset is just choosing to stay blind longer.

2) Decide: automate step-up controls where loss happens

Answer first: AI only helps if it triggers actions that actually prevent loss.

Your decision layer should be able to apply risk-based friction instantly:

• Step-up authentication (stronger MFA, re-verification)
• Withdrawal holds for high-risk events (short, targeted, defensible)
• Velocity controls that adapt to risk score
• Address allowlisting with behavior-aware exceptions
• Just-in-time limits for hot wallets

The trick is precision. Over-blocking customers is its own business risk. AI helps you apply friction to the 0.1% of events that deserve it, not the 20% your broad rules end up catching.

3) Respond: shrink “time to contain” with AI-assisted investigations

Answer first: You don’t stop theft just by alerting—you stop it by containing it.

When an alert fires, teams need fast context:

• Which identities and devices are related?
• What changed in the last 24 hours?
• Is this part of a cluster?
• Where are the funds moving next?

AI can:

• Auto-group alerts into incidents (reducing alert fatigue)
• Produce a machine-generated incident summary (“why this triggered”)
• Recommend containment actions (hold withdrawals, revoke keys, force re-auth)
• Prioritize by expected loss (not by alert severity labels)

This is where many organizations see the most immediate ROI: fewer analyst hours wasted on noise, and faster containment on real threats.

4) Harden: protect the signing path like it’s production money (because it is)

Answer first: The safest transaction is the one your system can’t approve without the right humans and the right machines.

Crypto theft often hinges on the signing workflow (hot wallets, MPC policies, key shares). AI doesn’t replace cryptographic controls, but it strengthens them by enforcing context-aware governance.

Strong patterns include:

• Segregation of duties: no single identity can both propose and execute critical actions
• Policy-based signing: risk score must be below threshold to sign automatically
• Anomaly-aware key operations: key share access that deviates from norm triggers step-up and human approval
• Continuous access evaluation: privileges adjust based on session risk and device health

Think of it as moving from “who are you?” to “who are you, what are you doing, and does it fit the pattern?”

“People also ask” questions security leaders are asking right now

Can AI stop state-backed hacking groups?

It can’t prevent every intrusion, but it reliably reduces blast radius. The win condition is: detect early, contain fast, and make theft operationally expensive. State-backed actors scale by repeating what works. AI breaks that repeatability.

Where should we deploy AI first: blockchain monitoring or account security?

Start where you can prevent loss fastest: account takeover + withdrawal controls. Blockchain monitoring is valuable, but it often triggers once funds are already moving. You want to stop the approval and execution steps.

What signals matter most for AI fraud detection in crypto?

High-signal inputs usually include:

• Session anomalies (new device + sensitive action)
• Changes to withdrawal addresses / beneficiaries
• Velocity spikes (many actions in short windows)
• Admin/API key events preceding withdrawals
• Destination novelty and graph proximity to risky clusters

What to do in the next 30 days (a realistic plan)

If $3.4B in crypto theft tells us anything, it’s that waiting for a “perfect security overhaul” is a luxury most teams don’t have.

Here’s a 30-day plan that’s achievable without boiling the ocean:

1. Map your top 10 “value exit” paths (withdrawals, swaps, admin signing, API disbursements)
2. Instrument event telemetry for those paths (who/what/when/where)
3. Deploy anomaly scoring for 3–5 high-loss event types (start small)
4. Add two containment playbooks that can run 24/7 (hold + revoke)
5. Measure outcomes weekly: time-to-detect, time-to-contain, prevented loss, false positives

If you can’t measure time-to-contain, you’re not running security—you’re running reporting.

AI in cybersecurity is becoming table stakes for fintech infrastructure

The Chainalysis figure—$3.4 billion stolen in 2025 through early September—is a hard reminder that digital value moves at attacker speed unless you design for defense at machine speed.

In this “AI in Cybersecurity” series, I keep coming back to one theme: AI isn’t here to replace security teams; it’s here to make them fast enough to matter. For payments and fintech infrastructure, that means real-time detection, risk-based decisioning, and automated containment around the moments where money can leave.

If you’re responsible for protecting customer funds, here’s the question that should drive your roadmap for 2026 budgeting season: Which happens faster in your environment—your attacker’s ability to move funds, or your ability to stop them?