How To Guides

Shadow IT in AI: How to audit AI infrastructure and discover unmanaged models, tools, and pipelines.
Secure ML Pipelines: Building a secure ML pipeline with GitHub Actions (MLSecOps best practices).
Jupyter Notebooks in Production: Why notebooks are dangerous in prod and how to harden them.
Zero‑Trust for AI: Applying Zero‑Trust principles to ML systems, data access, and model execution.
Detecting Shadow AI in Hugging Face Cache: How to audit developer endpoints for unvetted models and enforce license compliance.

Deploying AI Security in Air-Gapped Environments: How to deploy the Veritensor Control Plane and ML Workers in strictly isolated, internet-free networks.
Integrating AI Threat Alerts with Splunk and Jira: A technical guide to configuring webhooks and verifying HMAC signatures for SIEM and SOAR platforms.
Handling False Positives in MLSecOps at Scale: How Veritensor eliminates Alert Fatigue using the AI Verification Filter and Server-Side Suppressions API.

PII in Training Data: How to detect and remove personal data from AI training datasets.
AI Supply Chain Security: Meeting regulatory requirements with automated dataset scanning.
Data Poisoning Attacks: Preventing malicious data injection during LLM fine‑tuning.
Preventing PII Leakage in Synthetic Data: Implementing streaming Zero-Shot NER to sanitize LLM-generated datasets at scale.

Securing RAG Pipelines: Threat models and defenses for Retrieval‑Augmented Generation.
LangChain & LlamaIndex Security: Common misconfigurations and attack surfaces in LLM frameworks.
Using Models from Hugging Face Safely: Risks of untrusted models, weights, and configuration files.