As AI-generated media becomes more convincing, organizations need to extend verification beyond users and systems to the digital files behind critical decisions.
Deepfakes Are Exposing a New Trust Problem
For years, cybersecurity strategies have focused on protecting systems, networks, identities, and access to sensitive information. Zero Trust principles have helped organizations move away from automatically trusting users or devices simply because they are inside a corporate network.
But generative AI is exposing another challenge: Can organizations trust the digital content they receive?
Photos, documents, audio, and video increasingly influence decisions across insurance claims, lending, underwriting, financial transactions, fraud investigations, compliance, and other business processes.
Unfortunately, these files are becoming easier to fabricate, manipulate, reuse, or misrepresent.
Deepfakes have brought the issue into sharp focus. A convincing synthetic voice can impersonate an executive. An AI-generated photograph can depict damage that never occurred. A manipulated document can support a fraudulent transaction or claim. A realistic video can appear to provide evidence of an event that never happened.
As a result, verifying who is accessing a system is no longer enough. Organizations must also consider whether the information entering their business workflows can be trusted.
Analysts Are Calling for a Broader Approach
A 2026 report from Info-Tech Research Group, Defend Against Deepfake Cyberattacks, reinforces the importance of addressing deepfakes as a business risk rather than simply a technology problem.
The research emphasizes that deepfake detection tools cannot serve as a complete defense. Instead, organizations need a combination of people, processes, and technology, with verification practices embedded into everyday workflows.
Info-Tech also recommends extending Zero Trust principles beyond networks and systems to communications and transactions, where voices, videos, and messages can no longer be assumed authentic.
This is an important shift in thinking.
Deepfake detection remains valuable, but identifying suspicious content is only one step. Organizations also need processes for determining whether that content is trustworthy enough to support a business decision.
Source: Info-Tech Research Group, Defend Against Deepfake Cyberattacks (2026), pp. 2–3, 9.
Zero Trust Should Extend to Submitted Files
Traditional Zero Trust security asks organizations to verify access rather than assume trust.
The same principle can be applied to submitted digital files.
Don’t automatically trust a file simply because it looks authentic, comes from a familiar source, or has passed through an approved channel.
A customer may upload a photograph through a legitimate insurance portal. A borrower may submit financial documents through an authenticated lending application. A vendor may provide an invoice using an established business process.
In each case, the identity or access channel may be legitimate, but that does not necessarily establish the integrity, completeness, or accuracy of the submitted material.
A file may be:
AI-generated or synthetically altered.
Manipulated to change important details.
Reused from a previous transaction or claim.
Missing required information or supporting documentation.
Inconsistent with metadata, timelines, or business records.
Authentic in appearance but unrelated to the decision it is supposed to support.
This is why file validation must go beyond simply determining whether a file is a deepfake.
The more important question is whether that file can be trusted for the specific decision it is intended to support.
Detection Is a Signal. Validation Is the Workflow.
Deepfake detection technologies analyze files for signals of AI generation or manipulation. These signals can be extremely useful, particularly as synthetic images, voices, videos, and documents become more realistic.
But detection alone cannot resolve every issue.
Consider an insurance claim containing a photograph of vehicle damage. The image may not be AI-generated, but it might have been reused from a different claim.
A submitted invoice may be a genuine document, yet contain altered totals, duplicate charges, or unsupported line items.
A financial statement may appear authentic but contain inconsistencies with other submitted documents or transaction information.
In each example, the challenge is broader than detecting synthetic media.
A comprehensive file validation workflow can combine multiple checks:
AI and forensic analysis: Identify indicators of synthetic content, manipulation, and other file-level anomalies.
Metadata analysis: Examine available file information for inconsistencies or unusual characteristics.
Duplicate and reuse detection: Determine whether submitted files have appeared previously or in unrelated contexts.
Document validation: Identify missing information, mathematical inconsistencies, unsupported charges, or other content-level issues.
Business rules and contextual validation: Compare submitted materials against relevant policy, claim, transaction, customer, vendor, or workflow requirements.
Together, these signals help organizations make more informed decisions about submitted files rather than relying on any single detection score.
Verification Must Be Built Into Business Workflows
Another important takeaway from Info-Tech’s research is that organizations need practical verification processes, not just detection technologies.
For submitted file validation, that means integrating analysis into the business workflow before a file influences a high-risk decision.
Rather than treating every suspicious file as fraudulent, a validation workflow can help determine whether the submission should:
Pass: Continue through normal processing when validation requirements are satisfied.
Request more information: Seek clarification or additional supporting materials.
Route to review: Send flagged submissions to an appropriate analyst, underwriter, claims examiner, or fraud specialist.
Escalate: Apply additional controls before a higher-risk decision or transaction proceeds.
Importantly, these actions can be tailored to the organization’s business rules, risk tolerance, and operational requirements.
The objective is not to create unnecessary friction or require manual review of every submission.
It is to allow routine, well-supported submissions to move forward efficiently while directing attention to files that require additional scrutiny.
This approach becomes especially important as organizations pursue more straight-through processing and AI-assisted decisioning.
Faster automation should not mean faster acceptance of unvalidated information.
What This Means for Insurance and Financial Services
The need for submitted file validation is particularly relevant to industries that rely heavily on customer-provided information.
In insurance, submitted photos, videos, estimates, invoices, repair documents, and other supporting materials can influence claims, underwriting, fraud review, and payment decisions.
In financial services, uploaded financial statements, loan documentation, collateral photographs, transaction support, and dispute materials can affect lending, underwriting, investigations, and compliance decisions.
In both industries, the goal is similar: improve the reliability of digital inputs without slowing down legitimate business activity.
A layered validation approach can help identify suspicious, inconsistent, incomplete, or unsupported submissions earlier, reducing unnecessary manual review while improving the information available to decision-makers.
It also complements existing identity verification, cybersecurity, fraud prevention, and workflow automation technologies.
From Zero Trust Security to Digital File Trust
Deepfakes are forcing organizations to reconsider assumptions about digital authenticity.
Info-Tech’s research makes a compelling case for treating deepfake threats as a broader business resilience issue, requiring coordinated attention to people, processes, and technology.
For organizations that depend on submitted digital files, the logical next step is to apply similar verification principles to the photos, documents, audio, and video entering their workflows.
At Attestiv, this is the approach behind our DeepScan platform: combining AI detection, forensic analysis, metadata review, duplicate and reuse checks, document validation, and configurable business rules to help organizations evaluate submitted files before they influence critical decisions.
Deepfake detection is an important starting point.
But digital trust requires more than detecting a fake. It requires validating the files behind the decision.
Put File Validation Into Your Workflow
Attestiv helps organizations identify where automated file validation can reduce manual review, surface suspicious submissions, and improve decision confidence.
