AI Supply Chain

4 articles

Agent Security Agentic Permissions AI Agent Security AI Governance Evidence Ai Impact Ai Integration AI Red Teaming AI SDLC & Product Security AI Security AI Security Engineer Career Ai Security Engineering AI Security Foundations AI Security Monitoring AI Security Tools AI Supply Chain AI System Inventory Architecture and Trust Boundaries Ats Systems Attack Career Development Corporate Culture Corporate Culture And Leadership Culture Security Cyber Security cybersecurity Cybersecurity Strategy Data Exposure and Privacy Defend Detection Engineering Distributed Governance Distributed Systems Economic Governance Education Evaluation and Regression Testing Evidence Future of Work governance Governance And Resilience Governance Evidence and Customer Trust Governance, Risk & Compliance Hiring & Talent Hiring Strategy Incident Response & Observability Leadership And Governance LLM Application Security Logging and Telemetry Map MLOps & Platform Security Model and Provider Risk Model Supply Chain Operational Risk Organizational Governance Organizational Resilience Platform Governance Privacy & Data Protection Prompt Injection Psychological Safety psychometrics RAG Authorization RAG Security Recruitment And Talent Red Teaming & Evaluations red-team seceng-workbench Secure Architecture & Design Secure RAG Security Architecture Stochastic Governance Stochastic Resilience Systemic Resilience Talent Acquisition Technical Intelligence Threat Modeling Toolchain Integrity Training & Workshops Vendor Risk & Procurement Workforce Science Workplace Evolution

Defend

Notebook Security for ML and AI Teams: Jupyter, Colab, Databricks, and Hidden Execution Risk

Notebook security for AI and ML teams requires access control, secret management, data minimization, execution isolation, output review, dependency scanning, sharing controls, provenance, and promotion rules before notebooks influence production workflows or access sensitive data.

9 min read

Defend

Cloud Security for AI Workloads: GPUs, Secrets, Buckets, Model Endpoints, and Notebook Risk

Cloud security for AI workloads requires inventorying AI assets, protecting model endpoints, securing GPU and notebook environments, managing secrets, locking down object storage and vector stores, scanning containers, limiting egress, monitoring cost, and integrating AI infrastructure into normal cloud security operations.

10 min read

Defend

LLMOps Security: CI/CD, Secrets, Eval Gates, Model Registry Controls, and Deployment Promotion

LLMOps security requires CI/CD controls for prompts, tools, model configuration, provider routing, evals, secrets, registries, deployment promotion, monitoring, rollback, and governance evidence. AI release processes must track every artifact that can change system behavior.

10 min read

Defend

Securing Open-Source Models: What to Check Before Running a Model in Production

Open-source models require a production intake process covering provenance, license review, file formats, remote code, unsafe serialization, dependencies, containers, evals, serving infrastructure, monitoring, rollback, and governance evidence.

11 min read