Stop Synthetic Identities With AI-Powered Detection

Synthetic identity fraud isn’t “just another fraud problem.” It’s a direct attack on how your enterprise decides who gets access, who gets paid, and who gets trusted. In early 2025, synthetic identity document fraud jumped 300% in Q1, and deepfake-enabled fraud grew more than tenfold since the start of 2024. Those numbers aren’t scary because they’re big. They’re scary because they point to a future where anyone can show up as a “real person” on camera, on paper, and in your systems.

Most companies get this wrong by treating identity as a one-time hurdle—an onboarding checkbox, a KYC step, a background check. That mindset collapses when adversaries can generate convincing documents, fabricate professional histories, and even inject synthetic video into your verification flow.

This post is part of our AI in Cybersecurity series, and I’m going to take a clear stance: AI-generated synthetic identities require AI-assisted defenses—not because AI is trendy, but because the attack surface is now too fast, too scalable, and too human-looking for manual processes to keep up.

Synthetic identities are an enterprise security problem, not just a bank problem

Answer first: Synthetic identities threaten enterprises in two ways—financial fraud and trusted access (employees, contractors, vendors) that turns into data theft, sanctions risk, and long-term compromise.

When people hear “synthetic identity,” they often picture credit card fraud. That’s only half the story. A synthetic identity is a digital persona stitched together from real data (stolen identifiers) and fabricated data (names, addresses, employment history). It can be “real enough” to pass many checks while still not representing a real person.

Here’s why enterprises are now prime targets:

• Remote hiring normalized distance. HR and hiring managers must validate people they never meet.
• Digital onboarding reduced friction. Many workflows optimize for speed and candidate experience.
• Identity proofing tools were built for yesterday’s fakes. They’re often decent at spotting low-effort fraud, weak against coordinated, AI-assisted identity building.

The “no victim” advantage that hides losses

Synthetic identity fraud is painful because there’s often no obvious individual victim to complain, dispute charges, or file reports. That changes the detection economics. Instead of a loud fraud signal, you get quiet operational weirdness:

• A new “employee” whose work output is inconsistent
• Unusual access times that don’t match claimed location
• Devices that look like remote tools or “laptop farm” patterns
• Accounts that age normally… until a big cash-out event

If your detection relies on complaints, you’re already behind.

Generative AI changed the identity game: deepfakes that “show up”

Answer first: Generative AI is a force multiplier because it creates synthetic personas that can pass document checks, video calls, and biometric verification, especially via deepfake injection attacks.

Classic deepfake threats were mostly “presentation attacks”—someone points a camera at a screen playing manipulated media. Many organizations built defenses around that.

Now the bigger problem is injection attacks. Instead of replaying a video to a webcam, adversaries feed synthetic media directly into the verification pipeline, making it look like the camera captured a live person.

In 2024, injection attacks reportedly spiked 783% year over year. That matters because it breaks a lot of the assumptions behind liveness checks.

Why this is spreading faster than your controls

Three practical reasons:

1. Free or cheap tooling: High-quality face synthesis, voice cloning, ID template generation, and resume fabrication are widely accessible.
2. Automation: Fraud rings can generate and test identity variants at scale, learning what passes and what fails.
3. Cross-channel coherence: They don’t just fake a passport. They build LinkedIn profiles, GitHub activity, references, and a consistent story.

The result is a synthetic identity that doesn’t just exist “on paper.” It joins meetings. It chats with managers. It participates in Slack.

The remote hiring scam you should model as a threat scenario

Answer first: The North Korean remote IT worker playbook shows how synthetic or semi-synthetic identities can become sanctions evasion + insider access, with real operational impact.

A high-signal case study is the widely reported North Korean IT employment scheme, where operators use stolen identifiers or “loaned” identities, plus fabricated professional footprints, to land remote roles. Some evidence indicates the use of deepfake techniques to pass remote hiring steps.

What makes this enterprise-relevant isn’t just the deception—it’s the operational model:

• They pursue legitimate jobs (W-2, contractor, vendor arrangements)
• They use “laptop farms” and local facilitators to appear geographically consistent
• They gain trusted credentials and access paths that blend into normal work

Reportedly, confirmed infiltrations impacted at least 64 US companies, and each worker has been estimated to generate up to $300,000 annually.

Why CISOs should care: it’s not only about fraud

Once a malicious operator has a corporate identity and access, the outcomes look like classic insider risk:

• Theft of source code, designs, product roadmaps, models, and customer data
• Persistent access via tokens, SSH keys, OAuth grants, and VPN credentials
• Pivoting into supply chain compromise (CI/CD, package repos, vendor portals)
• Extortion: steal data, then threaten exposure

This is where the “AI in cybersecurity” theme becomes real: AI isn’t just detecting malware anymore. It’s helping you determine whether the human on the other end is real.

Why detection fails: the uncomfortable truth about tools and people

Answer first: Detection fails because many organizations over-trust identity verification platforms, under-train staff, and don’t instrument identity as a continuously monitored system.

There are two failure modes that reinforce each other.

1) Tool overconfidence (especially around liveness)

Many teams assume their IDV vendor “handles deepfakes.” The reality is more nuanced. Liveness checks vary wildly, and injection attacks specifically target the verification pipeline in ways many defenses weren’t designed to catch.

If your process is “upload ID + quick selfie video + done,” you’ve built an identity perimeter that can be tested and defeated like any other perimeter.

2) Human detection is worse than people admit

A 2025 study reported only 0.1% of participants could correctly identify all synthetic media, and fewer than one in ten could recognize deepfake videos. Even worse, a meaningful portion of employees say they’d take no action even if they suspected something was off.

So yes, train people. But don’t expect people to be your primary deepfake detector.

What “AI-powered identity security” should look like in 2026

Answer first: The winning approach is continuous identity verification with AI-driven anomaly detection across biometrics, devices, behavior, and transactions—aligned to a zero-trust identity model.

Most orgs already use zero trust language for networks and endpoints. Identity needs the same treatment: never trust, always verify—not in a burdensome way, but in a risk-based, automated way.

Here’s a practical model I’ve found works: treat identity as a living signal, not a document.

Layer 1: Stronger proofing at the highest-risk moments

Not every interaction needs the same scrutiny. High-risk moments do:

• New hire onboarding (especially privileged roles)
• Contractor onboarding with admin access
• Bank account changes for payroll
• Privilege escalation requests
• Vendor portal access creation

For these moments, add friction intentionally:

• In-person or notarized validation for certain roles
• Multi-factor biometrics (not just face) with anti-injection controls
• Out-of-band verification (secondary channel confirmation)

Layer 2: AI-driven anomaly detection after access is granted

This is where AI earns its keep. The question isn’t “Did they pass KYC once?” It’s “Does this identity behave consistently with a real employee?”

High-value signals to monitor:

• Behavioral biometrics: typing cadence, mouse dynamics (used carefully with privacy controls)
• Device trust and posture: device fingerprints, OS integrity, EDR state, impossible travel
• Access patterns: unusual repo cloning volume, token creation, API key usage spikes
• Collaboration signals: odd meeting participation, camera patterns, repeated excuses to avoid video
• Remote tooling: unexpected use of remote access/monitoring tools, consistent with laptop-farm operations

AI helps by correlating weak signals into a clear risk score and pushing only the right cases to humans.

Layer 3: Media authenticity and “deepfake-aware” workflows

Video and voice will be part of more workflows in 2026 than they were in 2023. That means:

• Require deepfake-aware video verification for certain workflows
• Use challenge-response interactions that are harder to pre-generate
• Capture and validate sensor and device telemetry to detect injection-style tampering
• Consider cryptographic provenance where available (especially for executive comms)

A blunt opinion: if your exec-fraud controls still assume “voice on the phone = the CFO,” you’re exposed.

A practical checklist: reduce synthetic identity risk in 30 days

Answer first: You can lower exposure quickly by tightening high-risk onboarding, instrumenting identity signals, and connecting HR, IAM, and SecOps.

Here’s a focused 30-day plan that doesn’t require rewriting your entire stack.

1. Map your identity entry points

   • Hiring, contractor onboarding, vendor onboarding, customer onboarding, partner portals

2. Pick two “must-not-fail” flows and harden them

   • Example: remote hiring for engineers + payroll bank change requests

3. Add step-up verification triggers

   • Privileged access requests
   • Geo/IP anomalies
   • New device + high data access in first 7 days

4. Deploy continuous monitoring for identity misuse

   • UEBA-style detection for access anomalies
   • Alerts for remote access tooling associated with covert relay behavior

5. Create a single escalation path

   • HR + IAM + SecOps need one shared playbook for “suspected synthetic identity” incidents

6. Run a tabletop exercise

   • Scenario: a contractor passes onboarding, then starts cloning repos and creating tokens
   • Decide: who freezes access, who contacts the “employee,” what evidence is required

The goal isn’t perfection. The goal is to stop treating identity as “HR’s problem” and start treating it as an enterprise attack surface.

The compliance angle: sanctions risk is a board-level issue

Answer first: Synthetic identities can trigger sanctions violations and severe penalties, even if you “didn’t know,” so identity controls belong in compliance and procurement—not just security.

When a synthetic persona is used for sanctions evasion, the blast radius grows:

• Regulatory and legal exposure
• Procurement restrictions and audit findings
• Customer trust and revenue impact

If you’re in finance, defense, critical infrastructure, or high-tech, assume scrutiny increases. Strong identity verification and continuous validation will become a procurement requirement, not a nice-to-have.

What to do next: build an “identity threat detection” capability

Synthetic identities are the new front in enterprise cybersecurity because they turn trust into an exploit. AI is part of the problem, and it’s also how you scale a defense that works.

If you’re building your 2026 security roadmap, add a dedicated workstream for:

• AI-driven identity verification for high-risk moments
• Continuous authentication and anomaly detection for employees and contractors
• Deepfake-aware processes for hiring, support, and executive communications

The forward-looking question to ask your team is simple: If a well-funded adversary can fabricate a convincing human who passes our current checks, what would we notice first—and how fast could we act?