5 key components of deepfake threat mitigation: Reality Defender

When deepfakes attack, who you gonna call? This is the fundamental question posed in a new blog from Reality Defender CEO Ben Colman, who says deepfakes are testing the limits of traditional security incident response and exposing “a widening gap in preparedness.”

To help address the problem, the deepfake detection firm has published a guide that provides “a practical framework for building an enterprise-grade deepfake detection and response plan, tailored for the teams now responsible for turning awareness into action.”

The fraud game has changed, shifting from a reliance on malware or network intrusion to a model that exploits trust. “A cloned voice can pass legacy voice biometric systems,” Colman writes. “A fake video call can impersonate a company executive with enough accuracy to trigger a wire transfer or password reset.”

“Security leaders already understand that AI-generated voice and video are being used to impersonate executives, trick employees into high-risk actions, and bypass biometric systems in real time. The challenge now isn’t awareness – it’s ownership.”

In this case, ownership means a formal structure for responding to deepfake attacks, which tend to defy traditional security classifications in the same way they confound legacy fraud protections.

“In most organizations, there is no playbook for deepfake response,” says Colman. “A fraud analyst may receive a report of suspicious customer behavior, but lack the tools to analyze synthetic voice. A SOC analyst may catch an anomaly, but have no escalation path if a spoofed video is involved. The core issue is fragmentation. AI fraud doesn’t clearly belong to any one team – so it falls between them.”

Reality Defender’s plan for effective deepfake response has five core components. Top quality detection is the starting point. “Without a reliable signal that malicious AI-generated content is present, there’s nothing to respond to. Detection must go beyond metadata analysis or surface-level heuristics – it requires real-time scanning of communication channels to flag manipulation as it happens.”

Triage defines who reviews flagged communications, what warrants escalation versus dismissal, and other questions. “Ideally, detection platforms offer confidence scores that inform triage without overwhelming analysts.”

Escalation outlines the chain of custody, particularly important for impersonation attempts targeting executives, finance teams, or customer-facing staff.

Attribution and forensics allow teams to verify that a piece of media was manipulated, document evidence for internal or external review, and integrate that insight into ongoing fraud or threat intelligence workflows.

And communication and containment kick in when a synthetic attack reaches external stakeholders such as customers, partners or the public.

Coordinating team roles and responsibilities is key to closing vulnerabilities. “Establishing clear lines of responsibility across fraud, security, legal, and communications is critical but often overlooked. Instead of defaulting to informal escalation paths, organizations should define response owners by incident type, impersonation target, and communication channel.”

The response plan should be tested and trialed; “practicing how teams handle a deepfake voice call or a manipulated executive video helps reveal process gaps before real stakes are involved.

Security awareness also needs an upgrade. “Teams across fraud, IT and customer support should be exposed to real examples of AI-manipulated communications to understand their impact – and why human intuition alone isn’t enough to catch them.”

Voice fraud in banking leads to losses beyond the bottom line

A separate post on fraud prevention focuses on voice fraud in banking. “Voice fraud is no longer an emerging risk,” Colman writes. “It’s a proven, high-cost attack vector now actively impacting financial institutions.”

The damage goes beyond immediate losses. “AI voice attacks force organizations to invest heavily in crisis management and remediation efforts. This includes conducting forensic investigations to understand the breach, enhancing security protocols to prevent future incidents, and managing public relations to mitigate reputational harm.​ These activities require significant time, personnel, and capital.”

Hits to customer trust are another risk, as is running afoul of regulators.

To further help organizations, Colman outlines a “practical framework for calculating exposure across five key dimensions.” These are authentication surface area, frequency of high-value transactions, call center and agent resilience, the ability to detect AI-generated fraud, and regulatory and compliance sensitivity.

“When applied collectively, these five dimensions allow financial institutions to build a risk-weighted model of their exposure.”

GenAI collaborations beef up detection capabilities

Colman also offers his thoughts for an interview with Customer Experience Magazine, highlighting the threat to call centers, and the role of AI in proactive deepfake defense.

“Our AI-powered detection models can analyse vast amounts of data and identify subtle anomalies indicative of AI manipulation at scale,” Colman says. “Our collaborations with generative AI companies also provide us with early access to their models, allowing us to proactively develop detection methods.”

Article Topics

banking  |  biometrics  |  cybersecurity  |  deepfake detection  |  deepfakes  |  financial services  |  fraud prevention  |  generative AI  |  Reality Defender  |  synthetic data