Who Owns AI Fraud? How to Build a Deepfake Response Framework

Deepfakes are testing the limits of traditional security incident response — and exposing a widening gap in preparedness. According to Forbes, AI-enabled fraud is now driving $12 billion in annual losses globally. Attacks powered by AI have evolved from a niche concern to a systemic threat in just a few years, giving teams a small window of time to prepare.

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.

Despite rising urgency, most organizations still lack a formal structure for responding to deepfake attacks. AI-generated media doesn’t fit cleanly into existing frameworks. It bypasses legacy detection, moves between fraud and cyber, and blurs the line between technical compromise and social deception. When a deepfake attack lands, who owns the response?

This guide offers a practical framework for building an enterprise-grade deepfake detection and response plan, tailored for the teams now responsible for turning awareness into action.

AI Fraud Demands a Unique Response

Deepfake threats targeting communications don’t behave like traditional cyberattacks. They don’t rely on malware payloads or network intrusion. Instead, they exploit trust. A cloned voice can pass legacy voice biometric systems. A fake video call can impersonate a company executive with enough accuracy to trigger a wire transfer or password reset.

Security teams accustomed to phishing and endpoint compromise are suddenly being asked to investigate AI-manipulated media. And in most organizations, there is no playbook for deepfake response. 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. Fraud teams focus on transaction risk. Security teams handle systems and network events. Compliance monitors for policy breaches. When a deepfake is involved, none of these roles cover the full scope. The result: confusion, delays, and missed signals during critical incidents.

Core Components of an AI Fraud Response Plan

To move from reactive to prepared, organizations need a structured response plan built around how deepfake threats actually unfold. That plan should cover five core components:

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. Reality Defender provides this capability across channels, enabling early interception before fraud escalates.

Triage defines what happens next. Who reviews flagged communications? What threshold warrants escalation versus dismissal? Response teams must establish criteria for severity, credibility, and urgency to avoid both alert fatigue and missed threats. Ideally, detection platforms offer confidence scores that inform triage without overwhelming analysts.

Escalation outlines the chain of custody. If a deepfake impersonating a CFO is used to authorize a funds transfer, who gets notified? What immediate actions are triggered? Escalation paths must be mapped out in advance, particularly for impersonation attempts targeting executives, finance teams, or customer-facing staff.

Attribution and forensics are critical for closing the loop. Teams must be able 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. Without reliable attribution, incidents can’t be properly classified or remediated. 

Communication and containment come into play when a synthetic attack reaches external stakeholders — customers, partners, or the public. Legal, PR, and compliance leaders need to be involved in messaging and response. Delays or missteps in this phase can turn a containable incident into a reputational crisis.

Team Roles and Responsibilities

Because AI fraud straddles multiple disciplines, response plans must clearly define roles across departments. A coordinated approach is the only way to close the gaps that deepfakes exploit:

SOC and IR teams lead initial detection and triage, using tools like Reality Defender to surface synthetic media signals.

Fraud teams assess financial exposure and validate whether transactions or actions were triggered by synthetic input.

Compliance and legal teams ensure that regulatory obligations are met, and that records are preserved and reportable.

Communications teams step in when external messaging or brand protection is required — particularly if synthetic media is leaked or shared publicly.

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. AI-powered fraud doesn't just cross team boundaries. It exploits them.

Tools and Signals to Watch

Effective response depends on reliable, integrated detection. Deepfake alerts must be accessible to the teams who can act on them and routed through the systems they already use.

Reality Defender’s turnkey solutions offer flexible integration across voice and video channels. Whether the manipulation happens in a video call, a voicemail, or a customer service interaction, our models detect them in real time and at scale. Instant alerts can be pushed into any pre-existing workflows, making them visible to SOC analysts and automatable within broader security systems.

Detection also needs to extend to tools used by fraud and customer service teams. AI-generated voices are being used to manipulate account recovery through IVR systems or to bypass KYC in onboarding, requiring constant monitoring of all channels where deepfakes can create a breach. The goal is to turn every attempted breach into a usable, actionable datapoint, rather than a missed anomaly.

From Reaction to Readiness

Once a response plan is in place, it needs to be tested and refined — just like any other critical incident process.

Tabletop exercises should include synthetic media scenarios, not just ransomware or phishing. Practicing how teams handle a deepfake voice call or a manipulated executive video helps reveal process gaps before real stakes are involved.

Security awareness and training should evolve, too. 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.

Maturity metrics help track progress. Measuring factors like average triage time for synthetic alerts, false positive rates, and escalation speed for impersonation attempts can reveal operational gaps and guide continuous improvement.

Closing the Gap With Reality Defender

Reality Defender provides award-winning deepfake detection solutions across critical communication channels, delivering real-time alerts into the workflows teams already use. Our platform is designed for enterprise-scale integration, enabling SOC teams, fraud analysts, and compliance leaders to identify and act on deepfake threats before they escalate.

From detection to triage, from evidence logging to alerts, Reality Defender closes the visibility gap that deepfakes exploit. With a structured response plan in place and the right tooling behind it, security teams can move beyond reactive posture — and contain AI fraud before it inflicts billions more in damages.

To explore how Reality Defender secures critical communications against deepfake attacks, schedule a demo with our team.