Enterprise security in a world of AI deepfakes and uncertainty over identity

Security has always been a battleground for enterprises. Attackers only need to succeed once, while organizations must defend every access point.

Now, AI has shifted the balance. The same generative technologies powering productivity, personalization, and automation are also being weaponized to undermine trust. For security leaders, the challenge is no longer just about keeping pace with threats, it’s about rethinking how trust itself is established in an AI-driven world.

The deepfake problem is no longer theoretical

Deepfakes were once treated as a novelty or a reputational risk confined to social media. Today, they are a direct and growing threat to enterprise security, particularly in identity verification, fraud prevention, and access control.

The barrier to entry is alarmingly low. Research cited by MIT Technology Review suggests it can take less than a minute to generate a convincing deepfake using freely available tools. At the same time, human detection rates hover between 50% and 59% — barely better than a coin flip. That gap between ease of creation and difficulty of detection is precisely where attackers thrive.

The impact is already being felt. Deloitte reports a 700% increase in deepfake incidents in the fintech sector during 2023 alone, while AARP and Javelin estimate that US banking customers lost $47 billion to identity fraud in 2024. These are not edge cases; they are signals of a systemic shift in how fraud is executed.

As generative AI continues to improve, deepfakes are becoming more realistic, more scalable, and harder to distinguish from legitimate users. Static defenses and single-point checks that once provided confidence are increasingly unreliable in isolation.

Why traditional identity checks are failing

Most identity verification systems were designed for a different era. Today, injection attacks, virtual cameras and AI-generated identities allow fraudsters to bypass isolated controls without ever appearing suspicious to legacy systems.

The issue is not a lack of innovation, but fragmentation. Point solutions assess risk in isolation, leaving security teams to stitch together signals after the fact. In practice, this means genuine users may be blocked while sophisticated attackers slip through undetected.

What’s required instead is a holistic approach that evaluates trust across the entire verification journey, rather than at a single moment in time. Solutions like Incode’s Deepsight are built around this principle, operating quietly in the background to analyze multiple perception layers in real time without introducing friction into the user experience.

Accuracy without friction

One of the most persistent tensions in enterprise security is the trade-off between protection and usability. Add too much friction, and legitimate users abandon onboarding flows. Reduce friction, and attackers exploit the gaps.

Advances in multi-modal AI are helping to resolve that tension. By analyzing depth, motion, visual consistency, device signals, and behavioral cues modern systems can identify subtle indicators of manipulation that would be invisible to humans or single-frame checks.

Independent testing underscores the importance of this shift. In evaluations led by Purdue University’s study Fit for Purpose? Deepfake Detection in the Real World, Deepsight demonstrated a significantly lower false-acceptance rate than other leading academic, government, and commercial systems, outperforming 24 alternative detectors.

Deepsight was 10 times more accurate than trained human reviewers, confirming that advanced AI defense is now essential to counter advanced AI attacks. In practical terms that means fewer fraudulent approvals without an increase in false rejections, an outcome that directly supports both security and growth.

“We evaluated nine of the most widely used commercial deepfake detection systems and found that Incode’s detector achieved the highest accuracy in identifying fake samples. This outcome suggests that Incode demonstrates stronger robustness and reliability in challenging real-world scenarios,” said Shu Hu, assistant professor at the School of Applied and Creative Computing and the director of the Purdue Machine Learning and Media Forensics (M2) Lab at Purdue University.

Crucially, these capabilities are delivered without changing the end-user journey. There are no additional steps, delays, or prompts. Security is strengthened but remains invisible to genuine users.

Trust as a strategic business asset

As digital interactions increasingly replace in-person verification, trust has become a core business differentiator. Customers expect fast, seamless experiences, but they also expect their identities, data, and finances to be protected.

A single successful impersonation attack can have consequences far beyond immediate financial loss. Regulatory scrutiny, reputational damage, and erosion of customer confidence often follow. In highly regulated sectors such as financial services, the stakes are even higher, as deepfakes threaten the effectiveness of traditional know your customer (KYC) and anti money laundering (AML) controls.

Multi-layered detection plays a critical role here; by improving confidence in identity verification outcomes, enterprises can strengthen compliance postures while reducing the operational burden on fraud teams. Instead of reacting to incidents after the fact, organizations can prevent sophisticated attacks from succeeding in the first place.

This is particularly important as regulators and industry bodies grapple with the implications of AI-driven fraud. Controls that go beyond current standards, incorporating behavioral analytics, device inspection, and injection detection, are quickly becoming a baseline expectation rather than a differentiator.

Continuous evolution in a fast-moving landscape

One of the defining characteristics of AI-driven threats is how quickly they change. Models improve, techniques adapt, and attackers experiment relentlessly. Static defenses quickly become obsolete.

That reality places new importance on technology partners who can evolve alongside the threat landscape. Solutions must be updated continuously, informed by real-world attack patterns and ongoing research, rather than relying on fixed rules or periodic updates.

Incode’s Trust Platform reflects this philosophy, with Deepsight designed to integrate seamlessly into existing identity verification flows and receive automatic updates as new threats emerge.

For enterprises, this reduces operational overhead while ensuring defenses remain effective against the latest forms of AI-enabled fraud.

Organizations such as credit rating agency Experian have integrated Deepsight into their identity and fraud solutions, highlighting the growing recognition that deepfake detection must move beyond isolated checks and into the core of verification infrastructure.

"Our work with Incode brings their Deepsight deepfake detection directly into Experian’s identity and fraud solutions, giving our clients market-leading protection and keeping them one step ahead of AI-powered attacks,” Keir Breitenfeld, SVP Identity and Fraud, Experian, commented.

Staying one step ahead of AI-driven fraud now and in the future

While the arms race between defenders and attackers is nothing new, the speed and scale at which threats can be created and deployed has changed dramatically. Deepfake attacks are no longer fringe tactics, they are becoming mainstream tools for fraud.

For enterprise security leaders, the path forward lies in embracing layered, adaptive defenses that reflect how modern attacks actually work. By combining perception, behavior, device, and integrity signals in real time, organizations can move from reactive detection to proactive prevention.

In an era of increased AI sophistication, trust cannot be assumed. And identity must be continuously verified quietly, accurately, and at scale. Enterprises that recognize this shift, and invest accordingly, will be far better positioned to protect their customers, and their brands.

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