The Future of AI Security Engineering: From AppSec to AgentSec to Autonomous SOCs
The future of AI Security Engineering is a platform discipline that extends AppSec into LLM applications, creates AgentSec for autonomous workflows, builds AI-native telemetry for detection and incident response, and turns governance into continuous evidence rather than annual paperwork.
The AI Security Buyer’s Guide: How to Evaluate Vendors for LLM Firewalls, Guardrails, Evals, and Monitoring
AI security buyers should judge vendors by the job to be done: filtering, testing, evals, access, logs, leaks, rules, and proof. Choosing a vendor should start with design and risk, not just labels.
Secrets Management for AI Apps: API Keys, Model Providers, Tool Credentials, and Delegated Access
AI applications need disciplined secrets management across model provider keys, vector stores, tool credentials, OAuth tokens, browser sessions, cloud keys, notebooks, logs, prompts, and agent runtimes. Secure design requires centralized secret storage, short-lived and scoped credentials, delegated authorization, redaction, rotation, revocation, and incident-ready evidence.
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.
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.
AI Logging and Telemetry: What to Capture Without Creating a Privacy Disaster
AI systems need logs because you cannot rebuild what happened from vibes. Security teams need to know what prompt was used, what docs were found, what the model said, what tool was called, who approved it, and what happened next.
Secure AI Product Design: How Product Decisions Create or Reduce AI Risk
AI product decisions can create or reduce security risk by controlling autonomy, data visibility, uncertainty, approval design, reversibility, source attribution, workflow placement, and abuse resistance. Product security must be involved early enough to shape the feature, not merely review it after launch.
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.
AI Evals as Security Tests: Building Regression Suites for Prompt Injection, Leakage, and Unsafe Actions
Security evals should test prompt injection, indirect injection, data leakage, RAG access, unsafe output, excessive agency, over-reliance, and cost abuse. These should be repeatable regression suites in CI/CD and governance evidence.
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.
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.
AI Data Governance for Security Engineers: Classifying Prompts, Outputs, Embeddings, and Training Data
AI data governance must classify prompts, outputs, embeddings, and training data. Security engineers need rules for provider use, retention, access, and deletion.
Vector Database Security: Access Control, Tenant Isolation, Poisoning, and Forensic Logging
Vector database security requires the same seriousness as other production data infrastructure, with additional attention to embeddings, metadata filtering, retrieval authorization, tenant isolation, poisoning resistance, deletion workflows, and forensic logging.
RAG Data Leakage: How Private Documents Escape Through Retrieval, Embeddings, and Context Windows
RAG data leakage happens when retrieval, embeddings, metadata, prompt context, generated answers, logs, or deletion workflows expose information outside intended boundaries. Secure RAG requires authorization-aware retrieval, tenant isolation, metadata filtering, sensitive-data minimization, protected traces, retention limits, and incident-ready evidence.
Least Privilege for AI Agents: Designing Permissions for Tools, APIs, Browsers, and Filesystems
AI agents need least privilege at the tool, API, browser, filesystem, credential, tenant, and action level. Safe design requires tool classification, read-only defaults, argument validation, scoped credentials, sandboxing, approval gates, and auditable enforcement outside the model.