What we learn assessing, building, and handing over AI agents, automation, and cloud infrastructure. Written by the people doing the work.
Evaluate DevSecOps engineers beyond scanner experience with CI/CD threat modeling, practical interview questions, and a technical hiring scorecard.
· 9 min read
Decide when to hire contract cloud engineers, permanent platform engineers, or a blended team. Compare delivery needs, cost, risk, and technical vetting.
· 9 min read
Learn how to hire and assess AI security engineers for enterprise agents, including identity, prompt injection, tool access, runtime controls, and practical interviews.
· 9 min read
Hiring engineers for agentic AI? Learn which engineering roles, production skills, and technical assessments matter when building reliable, secure enterprise AI agents.
· 11 min read
Why do IT staffing agencies struggle to vet engineers? Discover the risks of resume-based hiring and how Anpu Labs delivers engineering-led talent solutions.
· 8 min read
What abliteration research reveals about how open-weight models refuse, how to test refusal behavior responsibly, and why model alignment should never be your security boundary.
· 8 min read
Twelve questions CISOs should ask about agent identity, credentials, MCP, runtime isolation, data, model providers, network access, and incident response before production deployment.
· 4 min read
A practical method for turning agent workflows into least-privilege policies across files, network, credentials, tools, APIs, and enterprise resources.
· 4 min read
Combine NVIDIA NIM and OpenShell to separate private model inference from secure agent execution, creating a controlled architecture for enterprise autonomous AI.
· 4 min read
Autonomous agents need credentials to work, but they do not always need direct possession of reusable secrets. Learn how credential mediation changes the security model.
· 3 min read
Docker and NVIDIA OpenShell solve different layers of the agent security problem. Here is how container isolation and agent-aware policy fit together.
· 4 min read
Coding agents can expose enterprise data through model context, network egress, credentials, tools, and supply-chain behavior.
· 4 min read
A practical methodology for threat modeling autonomous AI agents across models, tools, credentials, networks, data, MCP, and enterprise systems.
· 5 min read
IAM remains essential, but autonomous agents introduce runtime, tool, credential, network, and execution risks that identity controls alone cannot fully address.
· 4 min read
MCP makes AI agents more useful by connecting them to enterprise tools. It also creates a new authorization surface security teams need to govern.
· 4 min read
NVIDIA OpenShell is not another agent framework. It is a security and runtime layer for controlling what autonomous agents can access and do.
· 4 min read
A practical enterprise security architecture for Claude Code: isolation, least privilege, network controls, credential mediation, policy-as-code, and auditability.
· 4 min read
AI agents can execute code, use credentials, call APIs, and modify enterprise systems. That changes the security model.
· 5 min read
Fixed scope. You get a systems map, a prioritized automation backlog, and a security gap list, whether or not you build with us.
