Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

How to Threat Model AI Agents in Kubernetes: A Practical Framework

Most threat modeling assumes the attacker has to break something. AI agents change that assumption. An attacker who controls a prompt can make the agent misbehave without breaking anything at all. The prompt can be a customer support ticket the agent reads, a document it retrieves, or a tool response it processes — any input the agent treats as context is an attack surface. On Kubernetes, that attack surface has physical form.

Deploying AI Agents to Production Kubernetes: A Security Checklist for Platform Teams

Your platform team already runs a production-readiness review on every workload that ships to Kubernetes. When the workload is an AI agent, the PRR doesn’t get thrown out — it gets a delta. Most of the items still apply; specific ones need extension when the workload is non-deterministic, calls tools dynamically, and exercises identity at runtime in ways the manifest didn’t predict.

GenAI security management: Governing apps, agents and MCP servers through central policy

Author: Alexander Ivanyuk, Senior Director, Technology Generative AI in business is no longer just one chatbot in one browser tab. In many environments, it is already a mix of web-based AI apps, built-in assistants inside larger platforms, internal agents created for specific workflows and model context protocol (MCP)-connected tools that let AI reach documents, services and business systems beyond the model itself. That changes the conversation completely.

RAG vs Agentic AI: What's the Difference and Why Does It Matter for Security?

Security architects who understood the large language model (LLM) risk two years ago are now confronting a more complex problem. The enterprise AI stack has split into two distinct architectural patterns, retrieval-augmented generation (RAG) and agentic AI, and the security posture required for each is fundamentally different. Conflating them is how programs end up with coverage gaps.

Measuring AI-Enabled Success: 3 KPIs Leaders Should Track

AI represents a fundamental shift in how organizations work and innovate. It demands an equally fundamental shift in how technology leaders approach governance. Forward-looking leaders are moving beyond traditional gatekeeping by creating "paved roads": secure, pre-approved pathways that embed security controls, automated data protections, and real-time monitoring directly into AI workflows so teams can innovate rapidly within safe boundaries.

Salt Cloud Connect for Github

Your developers are shipping agents, MCP servers, and APIs faster than security can see them. GitHub Connect changes that. Salt scans your repositories and surfaces every agent, MCP server, and API hiding in your codebase, then maps them into the Agentic Security Graph. You see the agentic infrastructure forming in code, before it ever reaches production. No more waiting for runtime to find out what shipped. No more blind spots between dev and prod. Govern what's being built from day one.

Is anything about AI worth the hype?

Dr. Adeel Shaikh Muhammad argues that when it comes to AI in the SOC, alert prioritization, anomaly detection, and SOC efficiency are where the real value is. The rest is mostly noise. On The Cybersecurity Defenders Podcast, the cybersecurity strategist and three-time author draws a clear line between where AI delivers and where the industry has oversold it. Full autonomous SOCs, perfect attack prediction, and replacing human analysts all fall on the hype side. AI narrows focus and accelerates decisions, but the final call still belongs to humans.

Analyze SMS phishing with an AI agent in Tines

Automate SMS phishing triage with AI — employees upload a screenshot, and Tines handles the rest in under 5 minutes. When employees forward suspicious texts, security teams still have to manually review screenshots, extract indicators, and route cases. This Five Minute Flow shows how to automate the entire process using the Tines AI action with Claude Sonnet — from employee submission to SOC case creation, IOC enrichment, and escalation when multiple employees report the same threat.

Securing AI agents: Why guardrail placement is a key design decision

When teams start building AI agents, especially with managed systems like Amazon Bedrock, they often wonder whether simply enabling guardrails is enough to secure their agents. A framework like Amazon Bedrock Guardrails provides a solid foundation for content filtering and policy enforcement, but having guardrails in place is only part of the equation.