Stop Synthetic Identity Attacks Before They Hit Revenue

Synthetic identity fraud isn’t “just a banking problem” anymore. It’s a full-spectrum enterprise security issue that starts as an identity event and ends as financial loss, sanctions exposure, and stolen IP.

The numbers tell you how fast this is moving: synthetic identity document fraud rose 300% in Q1 2025, and deepfake-enabled fraud grew more than 10x since early 2024. The uncomfortable part is that many organizations are still defending identity with checks designed for a world where attackers couldn’t manufacture a believable person on demand.

This post is part of our AI in Cybersecurity series, and it sits right at the center of the theme: AI is helping attackers scale fraud—and it’s also the best tool we have to detect patterns humans and static controls will miss. If you run security, fraud, IT, HR, compliance, or procurement, synthetic identities are now in your lane.

Synthetic identities are an enterprise problem, not a consumer one

A synthetic identity is a digital persona built from a mix of real and fake data—for example, a legitimate government identifier paired with a fabricated name, address history, social presence, and supporting documents. The identity looks consistent across systems, but it doesn’t map cleanly to a real human being.

What’s changed is the target. Attackers used to monetize synthetic identities primarily through credit-building and loan bust-outs. Now they’re using the same technique to walk into enterprises—as vendors, contractors, remote employees, or “trusted” customer accounts with elevated access.

Two realities make synthetic identities especially dangerous for enterprises:

• There’s no obvious victim to complain. When an identity is partly synthetic, traditional “someone reports it” detection breaks down.
• Enterprise processes are built on trust by default. Once you pass onboarding, you inherit an assumption of legitimacy—credentials, email, Slack, code repos, VPN access, purchase authority.

If you’re still treating identity verification as a one-time gate at account creation or hiring, you’re already behind.

The “dual threat” leaders miss: fraud plus state-backed abuse

Most teams frame synthetic identity risk as pure fraud. That’s only half the story.

Synthetic identities also support state-sponsored activity: sanctions evasion, illicit revenue generation, infiltration of supply chains, and IP theft. When synthetic personas become a workforce pipeline, the fraud problem becomes a national security and regulatory problem too.

GenAI made identity deception cheap, fast, and convincing

Generative AI didn’t invent fake identities. It removed the bottleneck: time, skill, and inconsistency.

Attackers can now generate, in minutes:

• Photorealistic profile images and “team photos”
• Resume narratives that match a target role and company stack
• GitHub-style code samples and commit histories (real or fabricated)
• Deepfake voice and video capable of passing casual scrutiny
• Synthetic biometric artifacts (face, fingerprints, iris patterns)

The most important technical shift is deepfake injection attacks. In 2024, injection attacks spiked 783% year over year. Unlike presentation attacks (holding a phone up to a camera), injection attacks feed synthetic media directly into the verification pipeline, making it appear as if the camera and microphone captured a real, live user.

That matters because a lot of “remote identity proofing” stacks were designed around a simpler threat model:

• scan an ID
• take a selfie
• perform a basic liveness action
• pass/fail

If the attacker can inject the stream, the whole ceremony becomes theater.

Why “we use a leading IDV tool” isn’t a plan

Many identity verification platforms market deepfake resistance aggressively, but independent testing has shown detection claims don’t always match real-world performance, especially against injection attacks.

Even worse: teams assume the tool’s green check means the risk is gone, so they loosen downstream controls—exactly where synthetic identities do the most damage.

A strong stance: treat identity verification as an input signal, not a final verdict. Your controls have to assume the attacker can occasionally beat the front door.

The remote hiring path is now a primary attack surface

The clearest enterprise case study is the North Korean IT employment scam, where operators posed as US-based engineers and landed remote roles across dozens of companies. Publicly confirmed cases have impacted at least 64 US companies, and reporting suggests the true number is higher.

Here’s what makes this playbook so effective:

• Hiring pipelines prioritize speed. Recruiters are measured on time-to-fill, not adversary resistance.
• Remote onboarding creates identity gaps. Hardware shipping, remote I-9 equivalents, async document checks, and contractor arrangements introduce seams.
• Once hired, access expands naturally. Tickets get approved, repos get shared, VPN exceptions happen, privileged roles are granted “temporarily.” Temporary becomes permanent.

Operational tactics like “laptop farms”—clusters of devices run by accomplices to mimic local employees—make location and network signals look normal. From a SOC perspective, the access pattern can blend into everyday remote work.

The real cost isn’t just wages—it’s sanctions and IP exposure

Synthetic hiring isn’t merely a “bad hire.” It can create:

• Sanctions violations (even if you didn’t intend to hire a sanctioned party)
• Regulatory fines that scale per violation
• Breach and incident response costs
• IP loss (source code, product roadmaps, designs, customer lists)
• Extortion risk (stolen data plus threats to expose)

Identity-related crimes have driven $8.8B in losses (2022), with $4.24M average loss per incident. Looking ahead, synthetic identity fraud is projected to contribute to $58.3B in annual losses by 2030.

The financial story gets attention. The supply chain and IP story should keep CISOs awake.

Why detection is failing: people, process, and signals

Synthetic identity defenses fail when organizations over-index on one layer.

Humans can’t reliably spot synthetic media

A 2025 study found only 0.1% of participants could correctly identify all synthetic media presented to them. Fewer than one in ten recognized deepfake videos. In the enterprise, that translates into a brutal truth: training alone won’t solve this.

Worse, even when employees suspect something is off, reporting can be weak—29% say they’d take no action. That’s not a people problem; it’s a workflow problem. If reporting is inconvenient, ambiguous, or socially risky, it won’t happen.

Static identity checks don’t match a dynamic threat

Most companies still anchor identity assurance in a one-time event:

• initial KYC
• background check
• onboarding verification
• first login MFA

Attackers exploit what happens after:

• privilege creep
• new device enrollments
• password resets and helpdesk workflows
• access exceptions
• vendor portal expansions

A synthetic identity that survives week one can be devastating by week eight.

The missing piece: identity, device, and behavior correlation

Enterprises often keep identity signals siloed:

• HR has candidate artifacts
• IT has device and endpoint telemetry
• IAM has auth events
• SecOps has detections
• Compliance has sanctions screening
• Finance has payment patterns

Synthetic identities thrive in the gaps between those systems. Detection improves sharply when you correlate them and look for contradictions.

AI vs. AI: a practical defense model for synthetic identities

The goal isn’t “perfect deepfake detection.” The goal is attack economics: make synthetic identities expensive to create, hard to maintain, and quick to expose.

1) Shift from verification to continuous identity validation

A modern enterprise stance is simple:

Assume identity is a risk score that changes over time, not a box you check once.

What to implement:

• Risk-based authentication (step-up when signals change)
• Continuous session evaluation (not just at login)
• Behavioral baselines for employees, admins, contractors, and vendors
• Re-verification triggers when anomalies stack up (new country + new device + unusual repo access)

This is where AI in cybersecurity earns its keep: it can model normal behavior at scale and flag subtle drift.

2) Detect injection attacks with pipeline integrity controls

If you rely on remote identity proofing, you need controls that address injection specifically.

Practical moves:

• Device attestation and trusted capture paths (prove the camera/mic feed is from the device, not a virtualized stream)
• Tamper detection on the client (virtual camera drivers, suspicious processes, remote control tooling)
• Challenge diversity (vary prompts and timing to resist scripted responses)
• Multi-channel confirmation (secondary device prompts or out-of-band verification)

AI detection helps, but integrity checks are the foundation. If the pipeline can be spoofed, your model is blind.

3) Treat remote hiring like privileged onboarding

Most companies get this wrong: they apply stronger controls to production logins than to the process of hiring someone who will receive those logins.

A tighter remote hiring control set:

• Delay provisioning until higher-assurance verification is complete for high-risk roles
• Notarized or in-person validation for privileged access paths (finance, IT admin, security engineering)
• Structured “fraud signals” review inside TA workflows (resume inconsistencies, unverifiable histories, duplicated portfolios)
• Device-first onboarding: ship managed hardware, enroll EDR early, and block BYOD for sensitive roles

If you’re scaling hiring in early 2026, budget time for this. Fast hiring that ignores identity risk becomes slow incident response.

4) Use AI anomaly detection where synthetic insiders can’t fake consistency

Synthetic personas can generate documents and faces. They struggle to maintain long-term operational consistency across systems.

AI models can flag:

• Access pattern anomalies (odd repo traversal, unusual data staging, off-hours privilege actions)
• Network contradictions (claimed location vs. device geosignals, latency patterns, ASN mismatches)
• Tooling fingerprints consistent with laptop farms or remote access intermediaries
• Helpdesk manipulation patterns (frequent resets, identity-proofing edge cases)

The win condition is fast containment:

• auto-step-up authentication
• temporary access freezing
• mandatory live re-verification
• targeted device quarantine

5) Bring threat intelligence into identity workflows

Synthetic identity campaigns repeat infrastructure, personas, and operational habits. Threat intelligence becomes useful when it feeds decisions in real time.

Where it should land:

• HR and vendor onboarding queues (screening and escalation)
• IAM risk engines (known bad patterns, compromised credential intelligence)
• SOC triage (context that turns “weird login” into “priority incident”)
• Compliance (sanctions and entity risk)

This is a strong “AI in cybersecurity” bridge: intelligence at scale is only actionable when automation routes it to the teams who can stop the transaction, hire, or access event.

A simple 30-day action plan for security leaders

If you want momentum without boiling the ocean, here’s what I’ve found works.

1. Map your identity attack surface: customer onboarding, vendor onboarding, hiring, contractor access, password resets, and payroll changes.
2. Pick 5 high-confidence signals you can correlate quickly (new device + new geo + privileged request + remote access tool + unusual data movement).
3. Add step-up and “stop-the-line” controls: when the stack of signals appears, force re-verification and freeze sensitive actions.
4. Harden remote hiring for privileged roles: managed device, higher-assurance verification, and security sign-off before access.
5. Run a synthetic identity tabletop with HR, IT, SecOps, and Compliance. If that group has never practiced together, that’s the first gap to close.

Where this goes next

Distinguishing real humans from synthetic personas is becoming a core business capability. The organizations that treat identity as a living risk model—validated continuously with AI-driven anomaly detection and strong verification integrity—will spend less time cleaning up fraud and more time shipping product.

If you’re building an AI-powered security program, synthetic identities are the proving ground. You’ll either use AI to connect identity, device, and behavior signals… or attackers will use AI to keep walking through the cracks.

What’s the one identity workflow in your company—hiring, vendor onboarding, customer KYC, or helpdesk resets—that would cause the biggest damage if it quietly failed next week?