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Glossary

AI Forensics: Evidence Handling With and On AI

AI forensics explained: how AI speeds up digital investigations and how to examine incidents on AI systems defensibly – artefacts, methods, limits.

Definition: AI forensics covers two closely linked areas of digital forensics. The first uses AI to sort through large amounts of evidence faster after a cyberattack. The second examines AI systems themselves when they have been attacked, tampered with or misused. Both follow the core rules of forensics: keep evidence intact, use methods others can repeat, and document everything.

Forensics with AI: making sense of large data volumes

Think of a ransomware attack or a hacked Microsoft 365 account. Afterwards, investigators often face millions of log entries, emails, file system records and memory images. AI-assisted tools help bring order to all that data:

  • Timeline reconstruction: The tools group and link events from EDR, firewalls, Active Directory and cloud audit logs. The result is a clear attack chain.
  • Triage: Models flag odd sign-ins, rare process runs or suspicious data leaving the network. So analysts know where to look first.
  • Text analysis: Language models sum up phishing campaigns, ransom notes or chat logs. They pull out clues such as domains, wallet addresses or file hashes.
  • Media analysis: They help spot fake audio and video, for example in CEO fraud with a cloned voice.

Be clear about the role of AI. It narrows the search. It does not write the expert opinion. Language models can make up links (hallucinations) or miss clues. A person must check every finding against the original evidence before it goes into a report, an insurance claim or a criminal case. Also, do not send evidence with personal data to outside AI services without proper controls.

Forensics on AI: artefacts inside AI systems

When the AI system itself is the crime scene, you look for different traces. Besides operating system and network evidence, you need to secure:

  • Prompt and response logs with the system prompt, conversation ID, user identity and timestamps. You need these to prove prompt injection.
  • Tool and agent calls: which functions, APIs or database queries an LLM agent really triggered.
  • Model artefacts with version and cryptographic hash. With these, you can show later tampering.
  • Training and fine-tuning data with records of where it came from, if you suspect data poisoning.
  • Vector databases in RAG systems. Which documents were added and when? Which ones were pulled up for a suspicious answer?
  • Supply chain: where downloaded models and libraries came from. Model files in pickle format can run code when loaded. Check them on their own.

One special trait: AI output often differs each time. If you run a prompt again, you will not reliably get the same incident. So it matters all the more that logs were captured in full, and safe from tampering, before anything happened.

Why it matters for mid-sized companies

Many mid-sized firms now use chatbots, copilot features or AI-based document handling. Often their logging was not built with a later investigation in mind. Prompt logs may be missing or get overwritten after a few days. Then you cannot tell how large a data leak was. Nor can you show supervisory authorities, insurers or courts what happened.

This affects, for example, how you assess a personal data breach under Art. 33 GDPR. For organisations covered by NIS2, it also affects the final report due within one month.

How to approach it in practice

  1. Get ready for forensics: Decide which AI logs you collect, how long you keep them and where you store them safe from tampering. Agree on this with your data protection officer.
  2. Secure before you clean up: Hash and copy the model version, settings, vector index and logs. Do this before you roll back a model or rebuild an index.
  3. Document the chain of custody: Who secured, handed over and analysed which data, and when?
  4. Keep AI tools under control: Where you can, run analyses in an isolated setting or with models you host yourself. Write down the tool version you used.
  5. Have people check the results: Trace every conclusion back to the original evidence. State open doubts in the report.
  6. Map attack techniques: Map what you saw to MITRE ATT&CK or MITRE ATLAS. This shows gaps and next steps in a clear, ordered way.

Conclusion

AI forensics joins classic evidence handling with new kinds of traces, such as prompts, model versions and vector indexes. What matters most is that the logs you need exist before an incident happens. SecTepe supports the forensic investigation of security incidents. We also help you decide which AI logs you should start capturing today.

Frequently asked questions

Can AI-assisted findings be used in court?

What counts is whether others can trace and check the findings. So use AI only for triage. The real evidence rests on the secured original data and a documented method.

How does AI forensics differ from an AI incident response playbook?

The playbook covers the whole response to an AI incident, from detection to recovery. Forensics is the part that finds out exactly what happened. It also secures the evidence so that it holds up.

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