Ask a security team what they log for an AI feature and the answer is often one of two extremes: everything, including full prompts and responses in plain text, retained indefinitely because someone might need it someday, or almost nothing, because logging felt like a feature-team concern and nobody explicitly assigned it. Neither extreme serves an investigation well. The first creates a concentrated store of potentially sensitive data that is itself a security risk. The second leaves an investigator with nothing to work from when something actually goes wrong.
A more deliberate approach logs specific categories of information, each for a specific reason, rather than defaulting to "everything" or "whatever happened to be easy to add."
Identity, always
Every AI interaction should be traceable to the identity that initiated it: a user, a service, or a workload identity, not a generic label like "the chatbot." Without this, an investigator cannot separate legitimate use from abuse, or determine whether an unusual pattern is coming from one compromised account or spread across many. This is the single most important field to get right, because almost every other useful question, who did this, how often, from where, depends on having it.
Policy decisions, not just raw requests
For any action that passes through a policy check, log what was decided and why: which rule was evaluated, whether it passed or failed, and what happened as a result. This is more valuable for security purposes than logging the raw request alone, because it tells you not just what was asked but whether the system's controls actually engaged and what they concluded. A log that shows a denial is often more interesting than a log that shows an approval, because it may represent an attempted policy violation worth investigating on its own.
Tool calls and their real-world effect
Every tool call an agent makes should be logged with enough detail to understand what actually happened: which tool, what parameters, and what the tool's response or effect was. This is the layer where a model's decision becomes a real action, and it deserves the same logging rigor an API gateway would apply to any privileged operation, because functionally, that is what it is.
Anomaly-relevant signals, tracked over time
Beyond individual events, security teams benefit from logging signals that only make sense in aggregate: rate of requests per identity, rate of denied actions, unusual sequences of tool calls compared to an agent's historical pattern, and shifts in latency or cost that might indicate a loop or automated abuse. These signals are what make it possible to detect a slow, low-and-slow attack pattern that would look unremarkable in any single log entry but stands out clearly across a window of activity.
What to avoid logging by default
Full prompts and responses, logged in plain text with no access restriction and no retention limit, deserve real caution. Prompts frequently contain the same sensitive customer or business data the rest of the security program is trying to protect, and a logging pipeline that captures it all by default has effectively created a second, often less protected, copy of that data. If raw content logging is genuinely needed for debugging or deeper investigation, scope it tightly: short retention, restricted access, and a clear justification for why metadata alone was not sufficient for the use case.
Credentials, API keys, and other secrets that might appear in tool parameters or responses should never be logged in plain text. This is not specific to AI systems, but AI logging pipelines are new enough that this basic hygiene sometimes gets missed during the rush to add observability to a fast-moving feature.
Retention should match purpose, not habit
Security-relevant metadata, like identity and policy decisions, often needs longer retention to support investigations and compliance reviews. Any raw content logging, if it exists at all, generally needs much shorter retention, because the risk of keeping it around accumulates the longer it sits unused. Setting retention deliberately by category, rather than applying one blanket retention period to everything a logging pipeline happens to capture, keeps the tradeoff between investigative value and data exposure risk intentional rather than accidental.
Access to logs is itself a security decision
A logging pipeline that captures useful security signals is not automatically safe simply because it exists. Who can query it, who can export it, and whether it is included in the same access reviews applied to other sensitive systems all matter. An AI activity log with broad, unreviewed access can become exactly the kind of concentrated, under-protected data source that the rest of an AI security program is trying to prevent elsewhere in the system.
Where TELEON fits
TELEON's audit trail is built around the metadata-focused approach described here: identity, policy decisions, tool calls, and outcomes, captured consistently across agents and model providers where TELEON is deployed through the gateway or supported middleware, without defaulting to storing full raw prompts and responses.
Decisions about whether any additional raw content logging is needed for a specific use case, and how tightly to scope and retain it, remain choices the deploying organization has to make based on its own risk tolerance and regulatory obligations.
A short logging checklist
- Log the initiating identity on every AI interaction, without exception.
- Log policy decisions and their outcomes, not just raw requests.
- Log tool calls with enough detail to reconstruct their real-world
effect.
- Track anomaly-relevant signals in aggregate, not just individual events.
- Avoid logging full raw prompts and responses by default; scope any
exception tightly.
- Never log credentials or secrets in plain text.
- Set retention by category, and review who has access to the logs
themselves.
Good AI security logging is deliberate about what it captures and why, not maximal. The goal is a record detailed enough to support a real investigation, without becoming a second, unprotected copy of the sensitive data the rest of the program exists to protect.