Kubescape

Automated AI security testing and red teaming platform

Mindgard is an automated AI security testing platform that provides continuous red teaming for machine learning models and LLM applications. It detects vulnerabilities including adversarial attacks, prompt injection, data poisoning, and model extraction through systematic probing. Offers end-to-end testing across the AI lifecycle with enterprise compliance reporting and integration into CI/CD pipelines.

Run local code inside your Kubernetes cluster without deploying

mirrord lets developers run local processes as if they were inside their Kubernetes cluster — intercepting network traffic, environment variables, and file access at the OS level without any deployment or configuration changes. Backed by $12.5M in seed funding with investors including Sentry's co-founder, it claims up to 98% faster iteration cycles and 30% fewer production bugs by eliminating the gap between local and cluster environments.

Kubernetes operator for serving AI inference workloads

KubeAI is an Apache-2.0 Kubernetes operator for deploying and scaling AI inference workloads, including LLMs, embeddings, reranking, and speech-to-text. It gives platform teams OpenAI-compatible endpoints, model proxy/controller primitives, model caching, scale-from-zero behavior, and cluster-native resource management for self-hosted inference on Kubernetes.

Google’s open-source Kubernetes assistant that translates natural-language intent into precise cluster operations.

kubectl-ai is an AI-powered Kubernetes assistant from Google Cloud Platform. It acts as an intelligent interface for cluster work, translating operator intent into Kubernetes commands and workflows. The key distinction from reactive diagnosis tools is that kubectl-ai is designed as an interactive natural-language interface for planning and executing Kubernetes operations, with provider configuration and MCP-oriented workflows around the CLI.

Microsoft’s public-preview runtime governance toolkit for policy, identity, sandboxing, audit, and MCP security around AI agents.

Agent Governance Toolkit is Microsoft’s MIT-licensed public-preview toolkit for governing AI agent runtimes. It adds policy enforcement, zero-trust identity, execution sandboxing, audit, reliability, and MCP security-gateway patterns around tool calls and autonomous actions, helping platform teams move beyond prompt-only guardrails while preserving architecture review requirements.

Telemetry-aware AI code reviewer that checks how pull requests may affect real services.

Baz is an AI code-review platform focused on production-aware pull requests. Instead of only reading the diff, Baz connects code changes to application telemetry so reviewers can understand what endpoints, services, and runtime behavior may be affected. That makes it a useful complement to existing AI PR bots when the question is not just whether a change looks correct, but whether it could break a live system.

Microsoft’s pytest-native red teaming framework for turning AI agent safety findings into CI tests.

RAMPART is an open-source Microsoft framework for safety and security testing of agentic AI applications. It brings red-team findings into a pytest-native workflow so teams can turn prompt injection, unsafe tool use, and behavioral boundary failures into repeatable regression tests. The strongest aicoolies angle is developer workflow: RAMPART makes agent safety part of CI/CD instead of a one-off security review.

State-machine guardrails for controlling which tools AI coding agents can use at each phase.

Statewright is a guardrail layer for AI coding agents that uses explicit state machines to control what an agent can do at each stage of a workflow. Instead of relying only on prompt instructions, teams can model phases such as plan, implement, test, and review, then constrain tool access for clients like Claude Code, Codex, Cursor, opencode, and related MCP workflows.