AI in DFIR: The Hilarious Misfit Trying to Solve Non-Problems!
AI in DFIR is like using a flamethrower to light birthday candles—exciting but often unnecessary. Sure, it can help identify evidence, but first it needs training, and if the data is dodgy, so is the AI’s output. For tasks like creating investigative plans, sometimes old-school human smarts are the real MVP.
Hot Take:
**_AI in Digital Forensics and Incident Response (DFIR) is like that Swiss Army knife you got for Christmas – it’s shiny and full of potential, but you end up using it to open boxes and bottles most of the time._**
Key Points:
– AI’s role in DFIR is often overestimated, as creating investigative plans requires human insight.
– Training AI models with incomplete or incorrect data can lead to inaccuracies in DFIR.
– Automation might be a more practical solution than AI for many DFIR tasks.
– AI’s impact on zero-day exploits is more of a theoretical threat to unprepared organizations.
– The complexity of training AI across multiple environments makes it a tough nut to crack.