Protecting Against Synthetic Identities and Deepfakes

Cybersecurity in 2025 is facing a new breed of adversary: one that doesn’t always have a pulse. Synthetic identities and deepfake technologies have evolved from emerging curiosities to urgent threats, capable of bypassing security systems, defrauding financial institutions, and tarnishing reputations in mere moments.

Synthetic identity fraud involves the creation of entirely fictitious personas using a blend of real and fabricated data—often stolen elements like Social Security numbers combined with fictitious names or birthdates.

Meanwhile, deepfakes leverage AI to manipulate audio and video content, creating realistic forgeries that can deceive both humans and machines. Together, they represent a significant challenge to organizations across industries—from finance and healthcare to government and retail.

What is Synthetic Identity Fraud?

Synthetic identity fraud is one of the fastest-growing types of financial crime in the U.S. The Federal Reserve defines it as the use of a combination of real and fake information to create a new, non-existent identity.

Unlike traditional identity theft, where a criminal steals and uses another person’s identity, synthetic identity fraud assembles a new persona that may take months or years to fully “grow” into a credit-worthy profile.

Key components typically used in synthetic identities include real Social Security numbers (often from children or deceased individuals), fictitious names and addresses, and fabricated employment or income records.

These identities are used to apply for loans, build credit, and eventually “bust out” with large financial gains before vanishing.

Synthetic identity fraud accounted for over $1 billion dollars in losses to U.S. lenders in recent years, with numbers continuing to rise as AI tools help scale these operations.

The Rise of Deepfakes in Cybercrime

Deepfake technology uses AI and machine learning—specifically deep learning algorithms—to create hyper-realistic fake videos or audio clips. Originally developed for entertainment and research, deepfakes have rapidly become a cybersecurity threat.

Cybercriminals are increasingly using deepfakes to impersonate executives in virtual meetings or to trick employees into wire fraud and data leaks. They’re also being weaponized for reputational attacks, creating fake videos that depict leaders saying or doing things they never actually did.

According to a 2024 study by VMware, 66 percent of security professionals reported encountering deepfakes as part of attack tactics—up from 13 percent just two years earlier.

Why These Threats Are So Dangerous

The potency of synthetic identities and deepfakes lies in their believability and adaptability. Both exploit gaps in traditional identity verification and detection systems that rely on static indicators (like document verification, facial recognition, or even biometrics).

Synthetic identities can build credible credit profiles over time. Deepfakes can fool facial recognition systems and even experienced personnel on video calls. In combination, they enable attackers to bypass identity verification, commit fraud, and maintain persistent access to sensitive environments.

How to Defend Against Synthetic Identity and Deepfake Attacks

Organizations need a multi-layered, proactive approach to protect against these evolving threats. As a result, the following strategies are critical:

1. Enhance Identity Verification Protocols

To effectively combat synthetic identity fraud, organizations need to implement layered, adaptive identity verification methods that go far beyond standard credentials. Traditional forms of identity proofing, such as usernames, passwords, or even photo ID uploads, are increasingly vulnerable to synthetic fabrication and deepfake manipulation.

A more resilient approach starts with behavioral biometrics, which track how a user interacts with a device, such as typing cadence, scroll velocity, mouse movements, and touchscreen gestures. These unique behavioral patterns are extremely difficult to mimic, even by sophisticated attackers.

Another key measure is device fingerprinting, which captures attributes like browser configuration, screen resolution, and installed fonts to create a unique device signature.

When matched with known user behaviors or usage locations, this information can help identify anomalous access attempts. Cross-referencing identity attributes, like SSNs, names, or dates of birth, against trusted government or commercial data repositories adds another safeguard by flagging inconsistencies and preventing the enrollment of entirely fabricated identities.

Additionally, facial recognition systems must go beyond static image matching. Incorporating liveness detection techniques—such as prompting users to blink, smile, or turn their head—ensures the system is analyzing a live person rather than a deepfake video or static image. Some advanced tools also analyze micro-expressions and facial texture changes to confirm authenticity.

2. Adopt AI-Powered Fraud Detection Tools

Modern fraud detection must operate at the cutting edge of artificial intelligence, leveraging the same kinds of machine learning advancements that threat actors now use to automate deception and manipulation. AI-driven fraud detection systems can parse through massive volumes of structured and unstructured data, quickly flagging subtle anomalies in behavior, usage patterns, and communications that would be nearly impossible for a human analyst to detect.

These tools excel in anomaly detection—for instance, flagging inconsistencies in financial transactions, login behavior, or geographic access patterns. A user who typically logs in from California and suddenly attempts to authorize a transaction from Eastern Europe without a travel indicator would raise a red flag in an AI-augmented monitoring system.

Moreover, natural language processing (NLP) enables AI to analyze emails, chat logs, and text-based documents for signs of phishing, social engineering, or impersonation. Metadata from email headers, timestamps, and sender histories can reveal spoofed or anomalous activity. These systems can also learn over time, adapting to emerging attack vectors and adjusting detection thresholds based on real-world incidents.

More recently, advanced tools have been developed to detect AI-generated media, including synthetic audio and deepfake video. These tools use neural network-based classifiers trained to spot imperfections in AI-generated content—such as unusual eye movement, frame transitions, or inconsistent voice modulation. When deployed in real time during video calls or content reviews, they can alert security teams to possible spoofing attempts.

3. Educate and Train Staff

No matter how advanced your technology, human vigilance remains a frontline defense. Cybercriminals are increasingly leveraging AI-enhanced social engineering tactics, making it crucial for organizations to prepare their staff to recognize and counter these threats. Security awareness programs should be ongoing, not one-time events, and include practical examples of phishing attempts, pretexting, and voice or video-based impersonation.

Employees should receive specialized training that includes real-world simulations involving deepfakes and synthetic identity schemes. Organizations should encourage the use of multi-channel communication verification for any high-risk or sensitive requests, particularly those involving financial transactions or credential changes. Incorporating these deepfake attack scenarios into incident response drills builds preparedness and helps employees develop reflexive, security-conscious behavior under pressure.

4. Strengthen Access Controls and Authentication

Access control must evolve in the face of AI-powered impersonation threats that can bypass traditional login defenses with alarming ease. Instead of relying solely on static credentials or simple two-factor authentication, organizations should implement risk-based authentication mechanisms.

These systems assess contextual signals such as location, device type, time of access, and historical behavior to determine if login attempts align with expected patterns.

For example, logging in from a known device at a routine time may allow streamlined access, while unusual behavior could prompt additional verification.

To enhance account security further, deploying hardware security keys—physical devices that verify identity through cryptographic communication—helps defend against phishing and man-in-the-middle attacks. Combined with public key infrastructure (PKI), these methods provide highly secure authentication for sensitive operations.

Organizations should also implement role-based access controls (RBAC) to limit privileges based on a user’s role and responsibilities. Coupled with continuous monitoring of user sessions, this ensures that suspicious activity can be flagged and acted upon in real time.

Lastly, it’s critical to minimize reliance on public-facing biometric data, such as face or voice prints shared in marketing or social media. Deepfake tools can easily scrape and manipulate this data to bypass facial recognition or voice authentication systems. Instead, consider combining biometrics with challenge-response techniques or using multimodal authentication—requiring multiple distinct factors—for high-risk scenarios.

Regulatory Landscape and Future Considerations

Governments and standards bodies are starting to take notice. In the U.S., the Identity Theft Red Flags Rule, Fair Credit Reporting Act (FCRA), and Know Your Customer (KYC) guidelines have been updated to emphasize more stringent identity verification protocols. These regulations now urge financial institutions, fintech platforms, and regulated entities to integrate behavior-based and dynamic authentication systems to detect synthetic identities before they are embedded into financial systems. Regulators are also encouraging the use of AI-enabled detection platforms to proactively flag discrepancies in identity creation and usage patterns.

On the deepfake front, the DEEPFAKES Accountability Act, although not yet enacted, has sparked global policy discussions around the labeling and traceability of AI-generated content. Meanwhile, the EU AI Act, which is set to be fully enforced in the near future, categorizes deepfakes as high-risk applications, requiring strict disclosure, traceability, and governance controls. Similarly, ISO 42001, the international standard for AI management systems, outlines best practices for identifying, mitigating, and auditing risks related to synthetic media. These evolving regulations are setting the stage for mandatory governance of deepfake technologies across industries.

Staying compliant isn’t just about avoiding regulatory penalties—it’s about building digital trust, protecting customers and partners, and future-proofing your operations against increasingly convincing forms of digital deception.

Secure Your Organization Against Emerging Threats

At RSI Security, we help businesses identify and neutralize sophisticated threats like synthetic identity fraud and deepfake attacks. From advanced penetration testing to deepfake detection and compliance advisory, our cybersecurity experts are here to help you stay ahead of tomorrow’s threats.

Contact RSI Security today to protect your organization from the next generation of digital deception.

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