IBM continues to put out great AI related learning content. This week they released a video that talks to why AI observability is so important for reliability and trust. The instructor in this episode is Jordan Byrd, he is a Product Marketing Lead for IBM.
Jordan identifies three key 'pillars' that observability needs to have:
- Decision Tracing
- Behavioral Monitoring
- Outcome Alignment
Together those three pillars give us Transparency, Visibility, Operational Control.
Jordan then explains what three types of information you need to get in order to do this:
- Inputs/Context
- Decision/Reasoning
- Outcome
He then talks about how this can be 'stitched' into a timeline and how does it stay aligned to what you wanted or whether there were anomalies. I am not going to go into it in more detail, as I want don't want to take away from you watching the video. I do want to make a comment on one of the last things he said because I feel it has a correlation to AI security.
The key takeaway is that AI observability isn't just dashboards or metrics. It's a full picture of the inputs, the decisions that the agent took and the outcomes. With those three things together, stitched into the timeline that we have, we can understand what the agent did, why it did it and build that transparent trail that you can trust, analyze and ultimately improve. That's what makes it possible to operate autonomous systems reliably at scale.
Jordan Byrd
What he is describing here perfectly matches up to what we do in a digital forensics investigation. It will be interesting to see how much planning this requires to get accurate data. Additionally, how much extra logging will be needed for this workflow. We all know that as much as some security vendors say otherwise, proper auditing and logging isn't cheap.
Head on over here to watch the rest. It is a TON of useful knowledge packed into an easily consumable 5 minute video!