Threat Modeling

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

Map

The AI Security Engineer Career Map: Skills, Tools, Frameworks, and Portfolio Evidence

The AI Security Engineer career path combines AppSec, cloud security, MLOps, LLM application security, secure RAG, agent security, red teaming, detection engineering, governance evidence, privacy awareness, and communication. Practitioners should build portfolio evidence that proves they can turn AI risk into controls, tests, telemetry, and operating decisions.

10 min read

Map

Threat Modeling LLM Applications: Data Flows, Trust Boundaries, Tool Calls, and Abuse Cases

LLM threat modeling should map assets, actors, data flows, trust boundaries, prompt assembly, retrieved content, model providers, tool calls, memory, outputs, identities, approvals, logs, and abuse cases. The output should become controls, tests, telemetry requirements, and incident-response assumptions.

10 min read

Attack

Building an AI Red Team Lab: Tools, Datasets, Harnesses, Attack Libraries, and Reporting Templates

An AI red team lab should provide a controlled, authorized, reproducible environment for testing LLM applications, RAG systems, AI agents, model endpoints, tool use, output handling, and governance evidence. It must include safe datasets, attack libraries, test harnesses, telemetry, evidence handling, reporting templates, and operational guardrails.

10 min read

Attack

The Agentic Anarchy Problem: Why AI Agents Break Traditional IAM Models

AI agents break traditional IAM because they act across user intent, application authority, and tool permissions. A secure agent program requires explicit identity, delegated authorization, scoped credentials, and policy enforcement that lives outside the model.

12 min read