AI security for cloud teams operating agentic workloads.

Inventory AI workloads, model endpoints, vector stores, data buckets, service accounts, SaaS connectors, and agent runtime environments.

Bind cloud credentials to expected repositories, branches, workflows, workloads, and environments instead of long-lived shared secrets.

Route model usage through approved gateways with team ownership, provider policy, token budgets, logging, and sensitive-data controls.

Monitor AI infrastructure in the same CNAPP and exposure graph as cloud identities, network paths, databases, containers, and Kubernetes.

Restrict egress from agent sandboxes, build runners, notebooks, and AI experimentation environments by default.

Prepare containment paths for compromised service accounts, leaked model keys, malicious automation, and cloud control-plane abuse.

AI in Security2026-04-24

Google Cloud and Wiz Want AI Security to Start Before the First Commit

Google Cloud Next 2026 and Wiz's April product updates make the same argument: AI security is becoming a code-to-cloud discipline built around agent identity, shadow AI visibility, and guardrails for AI-generated software.

AI in Security2026-05-15

AWS's New AI Security Framework Argues Day-One Controls Matter More Than Post-Launch Cleanup

AWS used mid-May 2026 guidance to make a useful point for defenders: secure AI programs start with identity, access, and guardrails in the prototype phase rather than after agents reach production.

Cloud2026-04-19

Vercel Breach Claims: What Security Teams Should Do About Environment Variables, Tokens, and Deployment Trust

Vercel confirmed unauthorized access to certain internal systems while hackers claimed to be selling stolen data. Security teams should avoid panic, but immediately review activity logs, rotate exposed environment variables, harden sensitive variables, and check GitHub, npm, and deployment tokens.

AI in Security2026-04-01

NIST's AI Agent Identity Push Gives Security Teams a Deadline and a Design Signal

NIST's February 2026 work on AI agent identity and authorization is a timely signal that the real enterprise risk is no longer model output alone, but what agents are allowed to do, prove, and audit once they start acting.

AI in Security2026-07-11

LiteLLM as the Central Gateway for GenAI and Agentic Code Models: Control, Vetting, and Token Discipline

Security teams do not need every product team wiring its own OpenAI, Anthropic, Bedrock, Vertex AI, and coding-agent credentials. A centralized LiteLLM gateway can make GenAI and agentic-code usage more controlled, vetted, auditable, and cost-aware.

Cloud security teams, CNAPP owners, vulnerability managers, and platform engineers2026-08-21

Wiz AI Security Skills for Cloud Security Teams

Practical AI-assisted Wiz skills for cloud security teams: exposure prioritization, attack path summaries, cloud risk ownership, and remediation planning.

Network security teams, cloud security teams, SOC teams, and firewall policy owners2026-08-21

Palo Alto Networks AI Security Skills for Network and Cloud Teams

Practical AI-assisted Palo Alto security skills for firewall policy review, Prisma Cloud risk summaries, Cortex investigations, and incident response.

CISOs, platform security teams, AI platform owners, and security architects2026-08-19

LiteLLM vs Portkey vs AWS GenAI Gateway: Centralized AI Gateway Comparison for Security Teams

A practical comparison of LiteLLM, Portkey, and AWS multi-provider GenAI gateway patterns for security teams centralizing model access, policy, logging, and token controls.

AI platform teams, security architects, model gateway owners, and CISOs2026-08-21

LiteLLM AI Security Skills for Model Gateway Teams

Practical LiteLLM security skills for centralized GenAI gateways: virtual keys, budgets, logging, routing, provider control, and agent governance.