The First 48 Hours: Where Deepfake Response Succeeds or Falls Apart
Editor’s Note: Synthetic media is forcing legal, cybersecurity, and investigative teams to reevaluate how digital evidence is authenticated, preserved, and defended. The challenge is no longer limited to identifying manipulated content; organizations increasingly need to demonstrate the provenance and integrity of digital information under legal and regulatory scrutiny. This article examines how deepfake incidents can expand beyond fraud events to encompass issues of chain of custody, regulatory exposure, evidence preservation, and operational coordination. Drawing from HaystackID’s webcast, “When Seeing Isn’t Believing: Deepfakes, Digital Evidence, and Proving Authenticity in the Age of AI,” the discussion focuses on the first 48 hours of response and the decisions that shape investigative and legal outcomes. The article also outlines five questions organizations should be prepared to answer when confronting suspected AI-generated evidence incidents. As courts, regulators, and enterprises adapt to synthetic media risks, evidentiary authenticity is becoming a more significant issue across investigations, compliance, and litigation workflows.
The First 48 Hours: Where Deepfake Response Succeeds or Falls Apart
By HaystackID Staff
Picture two glasses of water sitting side by side on a table. They look identical: clear, clean, and unremarkable. One is pure drinking water. The other has been contaminated with something colorless, odorless, and completely invisible.
Your instinct is to look for a difference. A tint. A particle. Something that signals which glass is the problem. John Wilson, Chief Information Security Officer and President of Forensics at HaystackID, used exactly this scenario to illustrate what legal teams are actually up against in the age of deepfakes, and why most organizations are asking the wrong question.
Wilson referred to it as “the detection instinct “—the assumption that better spotting tools will solve the problem.
“[Detection] is not the hard problem. The hard problem is this: how do you prove to a judge, a regulator, or a board that this glass—that the one you believe is real—is safe?”
Once contamination is known to exist, even the glasses you believe are clean cast doubt. You can’t see it. You can’t smell it. You can’t test for it with the tools you currently have. The presumption of safety is gone, not just for the contaminated glass, but for all of them.
“The existence of AI-generated content doesn’t just create false evidence. It contaminates the presumption of authenticity for all evidence,” Wilson said during a recent webcast, “When Seeing Isn’t Believing: Deepfakes, Digital Evidence, and Proving Authenticity in the Age of AI.”
That’s the shift legal technology professionals should internalize. The operative question is no longer whether a piece of digital evidence is fake; it’s whether you can prove, affirmatively, to a court or regulator, that it’s real.
The $25 Million Wake-Up Call
In early 2024, a finance employee at Arup’s Hong Kong office joined what appeared to be a routine video call with the company’s CFO and several colleagues. According to Hong Kong authorities and subsequent reporting, the participants were AI-generated impersonations created using deepfake technology. After initially expressing skepticism, the employee authorized 15 transfers totaling approximately $25 million. The fraud was later discovered when the employee contacted the company’s real headquarters.
While many incidents involve simpler manipulated audio, cloned voice messages, or edited media rather than fully interactive video calls, the evidentiary and authentication challenges remain similar.
Many portrayed what happened at Arup as a cautionary tale about the dangers of fraud. Wilson reframed it as an evidence story.
“The company’s lawyers had to prove that the video their own employees watched was fake, and they didn’t have the tools, the process, or the expertise to do that on their own,” Wilson said.
When Arup reported the incident, Hong Kong police confirmed it was one of multiple deepfake fraud cases under investigation in that same period. Federal law enforcement agencies, including the FBI and IC3, have increasingly warned about the use of synthetic media in fraud and impersonation attacks. In the UK, legal and policy bodies have begun examining how existing evidentiary frameworks apply to AI-generated content. Law enforcement is tracking it. Regulators are building frameworks around it. And courts are beginning to grapple with whether existing evidentiary standards are equipped to handle it.
Follow the Money, and Then Look Behind It
In most deepfake incidents, the money isn’t what the attacker is after. It’s the cover.
“While your fake CFO is explaining the urgency of the transfer, there may be something else going on. That deepfake may be a distraction to an end goal of grabbing other data on your network, or getting encryption keys, or some other highly sensitive piece of information,” said Todd Tabor, Senior Vice President of Forensics at HaystackID, during the webcast.
The fraud is often bait. In many incidents, the real objective may be to obtain data or credentials while everyone is focused on the wire transfer. That realization has significant implications for how legal and forensic teams scope their investigation.
Tabor identified three distinct exposure categories when the trust stack breaks down: fraud, data theft, and regulatory exposure.
- Fraud is the most visible: it’s the wire transfer, the misdirected payment, the items that show up on the balance sheet and trigger the initial call.
- Data theft is harder to see and often goes undetected until long after the incident closes: credentials harvested, sensitive files exfiltrated, network access quietly maintained.
- Regulatory exposure, according to Tabor, is where the longest tail lives.
“There’s personal data that could be accessed, definitely exposed. You have GDPR and state privacy clocks beginning to run. And if the material is not public, you have other issues,” he said, “You may have exposed yourself to SEC questions, regulatory industry questions, and financial stuff. The clock is moving, and you’re on it, whether you like it or not.”
Rene Novoa, Vice President of Forensics at HaystackID, added reputational damage to that list, a dimension that operates on its own timeline, independent of legal proceedings.
“Even though it’s fake and it’s not true, once it’s out there and you’re exposed in some sort of criminal activity, that’s what people believe,” said Novoa. “There are a lot of other things that can be very damaging to an organization when the trust fails.”
In many organizations, the larger issue is not just fraudulent media itself, but the erosion of trust in executive communications, internal approvals, and digital decision-making. Once employees begin questioning whether a voice, video call, or authorization request is authentic, the disruption extends beyond security response into governance, operations, and business continuity.
Detection is Only One Part of the Equation
The instinct in most organizations, when confronted with synthetic media concerns, is to deploy detection tools and look for artifacts, such as compression anomalies, generative model signatures, and metadata inconsistencies. That instinct isn’t wrong, but it’s insufficient, and misunderstanding its limits can be costly. Detection tools still play an important role in triage and investigation, but they cannot substitute for provenance analysis and forensic workflow discipline.
The forensics picture is more complicated than detection vendors typically acknowledge:
“It is very hard to say with certainty based on how digital media moves across our platforms,” said Novoa, sharing the example of metadata being stripped down when someone texts a video from an Android to an iPhone.
“The idea that metadata is removed from that source media does not detect that it’s AI or it was manipulated, but that is a process about how digital media, digital evidence moves through different platforms,” said Novoa.
In other words, the absence of metadata isn’t evidence of manipulation, and the presence of compression artifacts isn’t evidence of authenticity. Forensic investigators should understand how the data traveled, which platforms it passed through, and what was stripped away during normal transmission, before drawing any conclusions about deliberate alteration.
When media is fabricated from scratch, attackers can layer additional obfuscation techniques on top of the generated content to specifically defeat detection. That means the tools most organizations reach for first may return inconclusive results, or worse, false confidence.
The goal isn’t detection certainty in isolation. It’s building a forensically defensible account of what happened, one that traces provenance, establishes chain of custody, and constructs a timeline that can survive adversarial scrutiny.
That’s a meaningfully different capability than running a file through a detection tool and reading the output, and it’s the problem HaystackID’s Verification and Legal Identification/Authentication of Digital Media (VALID™) Suite was designed to address. VALID combines advanced analytics with disciplined forensic workflows and court-ready reporting to help organizations authenticate digital evidence, establish defensible timelines, and bridge the operational gap between forensic investigation and downstream legal, regulatory, and eDiscovery response.
The Five Questions That Define Your Response
The framework HaystackID’s experts have developed from deepfake incident response distills down to five questions every organization needs to answer within 48 hours of a suspected AI-generated evidence claim. They’re the questions that legal and forensic response hinges on, and the answers, or lack thereof, will define exposure.
Question 1: Is the media authentic?
This sounds like the obvious first question, but the forensic implications are substantial. Answering it requires more than running a detection tool. It requires getting as close to the original source data as possible, ideally, native files from the system where the content was created or first received, before it traversed platforms that strip metadata and introduce compression.
“When we look at just data that comes into my inbox or is presented to me on a hard drive, there is no way to authenticate just the media that’s just provided to me,” said Novoa.
Provenance matters. Authentication requires understanding how the file arrived, what it passed through, and whether any intermediate steps altered it in ways indistinguishable from deliberate manipulation.
Question 2: Were identities compromised?
The Arup attack involved real voices, real faces, and presumably real behavioral patterns; the AI generated a convincing enough simulation that trained employees, who knew their CFO, couldn’t detect the deception in real time. That level of fidelity requires source material. Which means the attack surface isn’t just the organization’s systems; it’s the individuals whose likenesses were used.
Identity compromise in deepfake attacks extends well beyond the immediate fraud target. As Novoa noted, synthetic identity creation is being used across financial services to defeat verification systems that rely on documents and biometrics that can themselves be AI-generated. The question of identity compromise, answered within 48 hours, determines the scope of the investigation and notification obligations.
Question 3: What data was accessed?
This question is where the fraud-as-distraction dynamic becomes legally and operationally critical. The wire transfer was visible. The simultaneous network activity may not have been. Tabor’s point about encryption keys and sensitive data access during a deepfake call is a reminder that the forensic scope needs to be much wider than the surface incident suggests.
Answering this question within 48 hours requires forensic investigation and eDiscovery working in coordination: two functions that, in most organizations, operate separately. That handoff costs days. In a deepfake incident, those are days during which regulatory notification clocks are running; evidence is degrading, and the adversary may still have active access to systems.
In practice, that coordination challenge often becomes an ownership challenge. Legal, cybersecurity, privacy, compliance, and eDiscovery teams all play a role in the response workflow, but few organizations have clearly defined who leads when questions of authenticity, regulatory exposure, and evidence preservation collide.
Question 4: What regulatory exposure exists?
The answer to this question depends on what was accessed, who was affected, and where they are located, all of which flow from questions two and three. GDPR and state privacy law notification obligations are triggered by personal data exposure, not by the attack itself. SEC obligations turn on materiality. Industry-specific frameworks carry their own timelines and requirements.
Jeff Shapiro, HaystackID’s Managing Director for Europe, placed particular emphasis on the cross-border dimension, noting that the EU E-Evidence Framework, which becomes applicable in August 2026, introduces emergency disclosure timelines that, in certain circumstances, can compress response obligations to as little as eight hours.
Question 5: Can you prove the timeline?
Of all five questions, this may be the one most likely to expose gaps in an organization’s readiness. Authentication isn’t just a technical exercise; it’s a legal one. The ability to present a coherent, forensically defensible narrative of what happened, in what sequence, and how it affected which systems and individuals ultimately determines whether an organization can defend its response.
Wilson returned to the water analogy: “Your case files are full of glasses of water, and the question isn’t which ones are contaminated. The question is, can you prove which ones are safe?”
Proving the timeline means demonstrating when the synthetic media was introduced, what it affected, and what was done in response, and showing that the evidence supporting that narrative was preserved with an unbroken chain of custody from the moment the incident was detected.
The Gap That Matters Most
Most organizations can answer some version of these questions… eventually. The 48-hour window is where many organizations discover gaps in readiness.
Forensic investigation and eDiscovery are typically separate workflows with separate teams, tools, and engagement models. In a deepfake incident, forensics must preserve and analyze the evidence before eDiscovery can collect and produce it. That sequential handoff, when it’s not pre-planned and pre-resourced, costs one to three days at exactly the moment the chain of custody is most vulnerable, and regulatory clocks are most unforgiving.
The organizations that handle these incidents well aren’t the ones with the best detection tools. They were the ones who knew, before the call came in, which questions they needed to answer and who was responsible for answering them.
Wilson put it plainly at the outset: “You will leave knowing which of these questions your organization cannot answer today.”
That’s not just a forensic challenge. It’s an operational readiness challenge spanning legal, cybersecurity, privacy, and executive leadership teams.
About HaystackID®
HaystackID® solves complex data challenges related to legal, compliance, regulatory, and cyber requirements. Core offerings include Global Advisory, Cybersecurity, Core Intelligence AI™, and ReviewRight® Global Managed Review, supported by its unified CoreFlex™ service interface and eDiscovery AI™ technology. Recognized globally by industry leaders, including Chambers, Gartner, IDC, and Legaltech News, HaystackID helps corporations and legal practices manage data gravity, where information demands action, and workflow gravity, where critical requirements demand coordinated expertise, delivering innovative solutions with a continual focus on security, privacy, and integrity. Learn more at HaystackID.com.
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SOURCE: HaystackID