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Recording model deployments and IAM changes is not enough—attackers may disable logs, tamper with objects, or shorten retention periods. This article establishes an independent security domain, a digest chain, and gap detection.
Inference workers, evaluation jobs, and model builders should not share cloud keys. This article separates the audiences of source credentials, exchange requests, and output tokens, and uses profiles and resource policies to block identity confusion.
Saving the model name, Prompt, and final score is not enough to replay an evaluation. This article freezes data and label generations, the request sequence, the decoder, the runtime environment, the Evaluator, and the result set to form a verifiable run evidence root.
Feature voting and customer volume easily mislead a roadmap. This article builds an auditable AI feature evidence scorecard that puts problem frequency, outcome value, real commitment, reachable users, delivery capability, and risk into one decision record.
Model vendor keys, object storage credentials, and signing keys can't be rotated by hand. This article lays out an end-to-end rotation flow: asset inventory, dual-credential switching, dynamic refresh, revocation verification, and leak response.
Single QPS rate limiting cannot stop long-context and batch tasks from squeezing inference pools. This article designs tiered quotas, atomic accounting, fair queuing, burst allowance, and failure degradation to protect multi-tenant AI platforms.
A policy engine returning allow doesn't mean downstream executes with the same input and obligations. This article defines a verifiable decision receipt, a one-time acceptance point, and execution confirmation to block stale policy, parameter substitution, and fail-open bypass.
Swapping a model while reusing old thresholds, or staring only at overall accuracy, silently changes both blocks and false kills. This article freezes the classifier, calibrator, per-category thresholds, and action table as independent artifacts, and gates release on per-slice confidence upper bounds.
Permanent allowlists turn temporary business needs into invisible backdoors. This article models policy exceptions as bounded leases, tied to precise scopes, compensating controls, and usage receipts, and makes expiry, revocation, and renewal re-evaluation actually work.
Traditional APM falls short for nondeterministic, multi-step, token-hungry LLM apps. This article covers three layers: traces for each LLM/tool call, automated quality evals, and latency, token, and cost metrics and alerts, then shows how to wire them into the development loop and turn post-launch black boxes into debuggable, improvable AI systems.
AI requests naturally carry high-dimensional information such as model, tenant, prompt version, and toolchain. This article covers metric label layering, cardinality budgeting, OpenTelemetry attribute trimming, Prometheus acceptance criteria, and over-limit rollback.
The hard part of Function Calling is not getting the model to return a function name, but handling wrong tool selection, argument drift, duplicate execution, timeouts, and partial success. This article presents a complete design for tool contracts, executors, idempotency, retries, and confirmation mechanisms.