Detecting (and Questioning) AI-Generated Evidence

Why “AI Detection” Is Only the Beginning

As AI-generated content becomes more sophisticated, the question facing disputes teams is no longer whether AI is influencing evidence—it’s how to verify its role.

At London International Disputes Week (LIDW), one point came through clearly from our session, “My Downing Street Phone Was Stolen: Proving (or Disproving) GenAI, Hacks, and Ephemeral Communications in Disputes AI detection is not a binary exercise—and it’s far from foolproof.

The Illusion of Certainty

There is growing reliance on tools that claim to identify AI-generated content in text, images, and video. While these tools can be useful, they come with important limitations:

• Detection often relies on patterns or artifacts that can be altered or removed
• Different tools can produce conflicting results
• Even advanced techniques, like watermarking, are not universally reliable

In other words, a “positive” or “negative” result from a single tool should never be treated as definitive. While certain AI-generated content identifying tools like Google’s SynthID can be useful, there are limiting factors and false positives; reliance on a solo source of authenticity may not be deemed a forensically or scientifically sound method.

A New Type of Risk: Fabricated Evidence

More concerning is the growing ability to generate entirely synthetic evidence that appears authentic. This includes:

• Emails with realistic headers and timestamps
• Documents that mirror legitimate formats and language
• Media that includes subtle indicators, but may evade detection

These developments raise a fundamental question for disputes teams: How do you prove something is real when it looks perfectly authentic?

Moving from Detection to Validation

The answer lies in shifting from tool-based detection to holistic validation. That includes:

• Cross-checking metadata at the system and file level
• Tracing provenance across multiple sources
• Using multiple analytical tools to compare outputs
• Identifying inconsistencies in structure, timing, or origin

Most importantly, it means involving technical experts alongside legal teams to assess evidence credibility at a deeper level. Using C2PA (Coalition for Content Provenance and Authenticity) manifests as an addition to traditional metadata can support provenance, origin and edits, but as this is a relatively new standard, it may take time until it is widely adopted.

The Takeaway for Legal Teams

AI has changed the evidentiary burden. It’s no longer enough to challenge what’s in front of you. You need to understand how it may have been created. Defensibility now depends on:

• Layered validation approaches
• Expert-led analysis
• Clear, explainable conclusions

In the age of AI, trust in evidence must be actively built, not assumed. As AI-generated content becomes more prevalent, validating evidence requires more than tools, it requires coordinated expertise across data, forensics, and legal workflows.

Cimplifi works alongside legal teams to assess authenticity, validate evidence, and deliver outcomes that stand up under scrutiny.  To learn how we help ensure evidence is trustworthy and defensible, visit our website for more on our forensics, collections, and investigation capabilities.