In Part 1, we laid out why explainability is foundational for secure and trustworthy AI systems. But theory alone isn’t enough. In this second part, we get practical: How exactly do you audit an AI agent’s behavior?
Auditing means more than capturing logs. It’s about understanding what an agent did, why it did it, and whether that aligns with your policies and expectations. To do this effectively, you need two pillars: comprehensive logging and explainable monitoring models.
1. Log Everything the Agent Does
To ensure traceability and compliance, you must capture an agent's activity across multiple dimensions:
- Prompt/Response Pairs: What was asked and what the agent said in return.
- Tool Use: Which APIs, databases, or plugins were called—along with the inputs, outputs, and purpose.
- Planning and Decision Flow: Internal steps such as subgoal selection, chain-of-thought reasoning, and dynamic plan revisions.
- Memory States: What the agent knew, remembered, or forgot—including both short-term and persistent memory snapshots.
- Visualization Dashboards: Search, filter, and investigate behaviors with context, making logs usable—not just collectible.
These logs provide the what—but not always the why.
2. Use Explainable Models to Monitor in Real Time
To shift from reactive to proactive security, augment logging with structured, explainable ML models that continuously evaluate agent behavior. These models can:
- Flag unsafe outputs or tool misuse
- Detect deviations from normal agent workflows
- Block or escalate policy violations
Unlike black-box LLMs, these models are deterministic and interpretable—they apply clear rules or classification logic that’s explainable to humans and auditable by machines.
Together with your logs, these models help security teams not only detect issues, but justify why they matter.
Why Generative AI Can’t Monitor Itself
It’s tempting to use an LLM to audit another LLM. After all, why not let a language model explain behavior it understands best?
Here’s why that’s risky:
- LLMs hallucinate — they can make up justifications that sound right but aren’t.
- They lack consistency — the same input can yield different results.
- They are slow — LLM takes a lot more time to run, then other machine learning models. For real-time ops every millisecond counts.
- Lack of clear metrics — You need something to drive automated action and another wall of text as is not it.
Instead, use structured ML classifiers, rule-based models, and anomaly detectors—tools purpose-built for reliability and governance.
Final Thought: From Audits to Accountability
Logs show you what happened. Explainable monitoring shows you what mattered—and why.
Combining both transforms auditing from a forensic exercise into a living, proactive security function. It’s not enough for AI agents to be powerful; they must also be accountable.
That’s the future of AI security. And that’s the Aiceberg way.
→ Ready to audit with explainability built-in? Book a demo and see how Aiceberg helps enforce, monitor, and verify your AI agents
Conclusion
When logs capture every move and explainable monitors make sense of it in real time, AI security shifts from reactive cleanup to continuous assurance—turning agent behavior from a black box into a transparent, enforceable contract. This fusion of traceability and clarity equips teams to spot misuse instantly, defend their policies with evidence, and build the trust regulators and customers demand. It proves that raw speed alone is empty without accountability, and that the real competitive edge lies in verifiable control. By treating auditing as an active discipline rather than a forensic afterthought, you create AI systems that not only perform but stand up to scrutiny—exactly the standard Aiceberg is championing.
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