The same word, “governance,” covers radically different realities on the open web, in closed environments, and in agentic systems. Interpretive governance must therefore be deployed contextually, not as a single recipe.
Archive
Blog — page 11
Paginated archive of Gautier Dorval’s blog.
Prompt Shields (Microsoft) can block certain jailbreak and indirect injection patterns. This doctrinal reading clarifies what it protects against, and what it does not replace.
In RAG, corpus contamination is not a peripheral accident. Retrieval turns fragments into contextual authority, which makes contamination a structural risk rather than a local defect.
Why semantic architecture aims to reduce the error space of algorithmic systems instead of correcting errors after they spread.
A produced interpretation becomes dangerous when it starts feeding future interpretations back as if it were already established.
SEO has not disappeared. Its problem space has shifted from local visibility to architectural intelligibility in an interpreted web.
Why silence remains an exception in AI systems, and why governed suspension should count as a high-quality output.
In AI systems, empathy stabilizes conversation. It becomes risky when relational style starts replacing evidence and restraint.
A generative system can access many sources and still remain indefensible if no hierarchy determines which sources prevail, which are secondary, and what happens when they conflict.
When AI systems keep returning an outdated state despite public updates: prices, inventory, policies, hours, and conditions.
Structured data is not primarily about visual enhancements. It is a way of making entities, relationships, and boundaries more explicit.
Field observations showing how informational silence becomes a trigger for inference and leads to persistent interpretation errors.
When informational silence becomes a trigger for inference, and why the absence of signal is never neutral in an interpreted web.
Why hierarchizing information is not a neutral editorial choice, but an act of governance that shapes interpretation.
“Summarize this” functions are not neutral. They force a system to ingest third-party content and can turn a legitimate task into an attack surface through role mixing.
Why every information structure implies exclusion, and how boundaries shape the way search engines and AI systems interpret meaning.
A plausible assertion without reconstructible justification is not only weak. It is a source of interpretive liability once it is reused, published, or relied upon.
In an interpreted web, correction is not enough. Why versioning becomes a strategic mechanism of interpretive stability.
Brand invisibilization is an early symptom of a deeper shift: AI systems are becoming decision infrastructure, and AI governance is emerging as a cross-functional strategic function.
AI does not create the flaws of today’s web. It reveals them, amplifies them, and turns them into actionable structural vulnerabilities.
Field observations on the real behavior of crawlers and non-human agents, and on what that behavior reveals about algorithmic interpretation.
“Not indicated” does not mean “unknown.” It means answering would require an unpublished deduction, an extrapolation, or an unauthorized interpretive reconstruction.
Contradiction is not the main problem. The real risk begins when a system silently arbitrates between contradictory sources and turns that arbitration into a single authoritative answer.
Field observation: in some contexts, an AI system suspends inference and asks for a canonical definition rather than completing the meaning.