Insights on AI governance and ML compliance
Practical guides for ML engineers and AI governance teams navigating regulated enterprise AI deployments.
Why ML Compliance Fails: The Gap Between Experiment Tracking and Audit-Ready Documentation
Experiment tracking tools like W&B and MLflow solve the ML engineer's problem — visibility into training runs. They don't solve the compliance team's problem: a signed-off, immutable record that can survive a regulatory review.
Dataset Versioning for Fine-Tuning: Why SHA-256 Hashes Are Not Enough
A SHA-256 hash of your training dataset tells you if it changed. It doesn't tell you what changed, when, who approved the change, or whether the change was intentional. Fine-tuning compliance needs all four.
LLM Model Risk Management at Banks: What SR 11-7 Requires in the Age of Fine-Tuned Models
The Federal Reserve's SR 11-7 guidance on model risk management was written for statistical models. How do its requirements apply to fine-tuned language models used in credit decisioning?
HIPAA-Adjacent AI: What Covered Entities Need Before Deploying a Fine-Tuned Clinical NLP Model
HIPAA doesn't directly regulate AI models. But the Business Associate Agreement, minimum necessary standard, and audit control requirements create documentation obligations that clinical AI teams consistently underestimate.
Adding an Audit Trail to Your Hugging Face Fine-Tuning Pipeline
A step-by-step guide to instrumenting a Hugging Face Trainer-based fine-tuning loop with Cognify for full dataset and run lineage. Under 30 minutes.
Eval Benchmarks Your Compliance Team Will Actually Trust
Hellaswag, MMLU, TruthfulQA — these benchmarks matter to ML researchers. Your compliance committee has never heard of them. Here's how to map standard LLM evals to compliance objectives.
EU AI Act for Fine-Tuners: What Documentation Does Article 53 Actually Require?
If you fine-tune a general-purpose AI model for internal enterprise use, Article 53 of the EU AI Act applies to you. The documentation obligations are specific — and most MLOps teams are not ready for them.
Designing an Immutable Audit Log for ML Pipelines: Append-Only, Hash-Chained, and Regulator-Ready
An audit log that can be edited is not an audit log — it's a history file. This post covers the architectural decisions behind Cognify's write-once append-only log.
Automating Model Cards for Regulated Industries: Beyond the Google Template
Google's model card framework is a starting point. Regulated industries need additional sections: data provenance attestation, bias evaluation against protected classes, and regulatory cross-references.
MLflow for Experiment Tracking, Cognify for Compliance: Why We Use Both
MLflow tracks experiments for ML engineers. Cognify tracks experiments for compliance teams. They solve different problems. Here's a concrete breakdown.
On-Premises LLM Fine-Tuning: Why Air-Gapped Environments Still Need Audit Infrastructure
Running your fine-tuning pipeline inside a VPC or air-gapped data center doesn't eliminate compliance obligations — it adds new ones.
Data Provenance for Fine-Tuning: The Six Questions Every Compliance Team Will Ask
Before any regulated enterprise can sign off on a fine-tuned model, compliance teams ask the same six questions about the training data. Most ML teams can answer none of them without significant manual work.
Designing Approval Workflows for ML Models: What Compliance Teams Need from the Sign-Off Interface
Compliance teams don't want to use ML dashboards. They want structured, auditable approval flows with e-signatures and immutable records of who approved what and when.
Instrumenting PyTorch FSDP Training with Cognify: Full Shard Lineage and Checkpoint Tracking
PyTorch FSDP distributes model shards across devices in ways that make standard checkpoint logging incomplete. This guide shows how to instrument FSDP training with Cognify to capture full shard lineage.
AI Governance Tooling in 2026: Where MLOps Ends and Compliance Begins
The AI governance tooling market has split into two distinct categories: ML observability tools and compliance-first documentation tools. This split is permanent, and the gap between them is exactly where regulated enterprises get stuck.