The Real Reason AI Safety Fails is "Log Worship" — Why Recording Everything Doesn't Make You Safe

Audit Logs Are Not the Reason AI Safety Fails: The True Nature of "Log Worship" Destroying Model Monitoring

"We are monitoring everything with a robust system. All logs are recorded, and we are fully ready for audits."

In the field of AI governance, these words are repeated constantly. However, they are arguably the most dangerous "indulgence" for safety. We are currently lost in a thick fog known as "Log-Centric Fallacy" (Log Worship)—the blind belief that the mere existence of logs equates to safety.

In this article, we will dismantle this widespread misunderstanding in the fields of AI safety and MLOps. We will discuss the true safety protocols required to upgrade logs from "mere materials" to "irrefutable evidence."

1. The Hubris of "We're Safe Because We Have Logs"

In AI operations, logs are treated as a sanctuary of security. "We record every request," "The dashboard is green," "We have an audit trail." Unfortunately, in the context of AI safety, these are surprisingly powerless.

This is because logs are nothing more than shadows of fragmentary facts—"what happened"—and they possess no power to determine "whether it was safe." Paradoxically, as logs accumulate, the "freedom of interpretation" increases, and a definitive judgment of truth drifts further away.

Let us get to the core of the matter. Logs are merely "materials." Safety is not guaranteed by the volume of logs, but solely by "evaluation procedures (protocols) that cannot be changed after the fact."

2. The Core Enemy: The Deception of "Post-hoc Justification"

When logs are used as the foundation for safety, the greatest obstacle we face is "Post-hoc Justification" (evaluation after the fact).

After an incident occurs, we unconsciously adjust our judgment criteria retroactively. "If we look at it through this metric, it was normal," "If we change the aggregation window, it's within the acceptable range," "The threshold setting at the time was just too loose."

As long as we continue to manipulate metrics and thresholds after an accident to justify the past, AI auditing becomes completely hollow. Speaking about the past based on "judgments made for current convenience" rather than "judgments made at the time" can no longer be called safety.

True AI safety must meet the following three "cold conditions":

1. Non-retroactivity (Impossible to alter post-hoc): The evaluation criteria must not move even a millimeter after the event.

2. Determinism: In the event of a deviation, a "black or white" judgment must be rendered immediately, without allowing for hesitation or rephrasing.

3. Reproducibility: Any third party using the same rules must arrive at the same verdict (judgment).

3. Why Logs Do Not Guarantee Safety: Seven Structural Failures

Why does the log-centric safety philosophy collapse? The reasons are concentrated in the following seven structural failures:

1. Arbitrariness of Recording: Logs are not reality itself; they are merely "the reality we decided to keep." Critical omissions always lurk in the "unrecorded regions."

2. Generation of Narratives: The more logs you have, the more variations of explanations (excuses) you can create. With a single filter, one can fabricate both a story of "success" and "failure" from the same log.

3. Substitution of Evaluation: Logs cannot stop the "mutation of evaluation criteria." In fact, the more comprehensive the logs, the easier it becomes to cherry-pick data that fits current arguments.

4. Fluidity of Meaning: If label definitions or business rules change, the meaning of past numerical values vanishes. Because the "meaning at the time of measurement" is not frozen, third-party reproduction becomes impossible.

5. Metamorphosis of Observation Tools: The logic that generates logs itself drifts. If the measurement system changes, comparisons with the past become meaningless noise.

6. Lack of Evidentiary Power: Logs without signatures or version history are nothing more than "text files." Auditing devolves into a "log submission contest."

7. Procrastination of Responsibility: Log-centricity institutionalizes the "grace period for not making a judgment." The phrase "let's check another log" indefinitely postpones the determination of responsibility.

4. The Solution: Shifting from Log-Centric to "Protocol-Centric"

How, then, can we transform logs into "evidence"? The answer lies in solidifying the "ground" (the evaluation protocol) before accumulating logs.

AI safety only stands up when the following "Minimum 7-Piece Set" is in place:

1. Pre-fixed Metrics: Define the calculation procedures completely.

2. Fixed Data Boundaries: Define what to evaluate and what to ignore.

3. Fixed Sampling Protocols: Do not allow arbitrary data extraction.

4. Prior Agreement on Thresholds and Exceptions: Do not move the goalposts after an accident.

5. Frozen Evaluation Code (Harness): Versioning via hash values.

6. Standardized Output Formats: In a form that allows third-party verification/calculation.

7. Regulated Change Procedures: Treat criteria changes not as "tweaks" but as "registration of a new protocol."

Manage evaluation definitions in Git just like source code, and freeze them with hash values for every release. Prioritize auditing "which protocol is currently applied" over the execution logs themselves. This is the only path to escaping the "Log Worship."

5. GhostDrift as a New Horizon

Here, I would like to mention a new concept we must confront: "GhostDrift."

Traditional monitoring has looked at "changes in data distribution." However, what is truly terrifying is not the data itself, but the silent progression of the "alteration of evaluation rules" and the "transformation of operators." The technology to detect this and turn logs into evidence through "Ledgering" is the true front line of AI governance.

GhostDrift is not merely a technical challenge. It is a new horizon where the front lines of the humanities—namely art and philosophy—intersect to capture the "transformation of meaning."

Summary

Logs are materials. Safety is established only through evaluation procedures (protocols) that cannot be changed after the fact. Our task is not to lament the lack of logs. It is to determine how coldly we can block "post-hoc justification" and freeze judgments. The future of AI safety depends entirely on this single point.