Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

armo

The CISO's AI Agent Production Approval Checklist: 7 Gates to Clear Before Go-Live

Apr 10, 2026 By Shauli Rozen In ARMO
Your engineering lead is in your office Thursday morning. They want to push an AI agent to production next Tuesday. It’s a LangChain-based workflow agent, connected through MCP to three internal tools and one external API, with access to a customer database. The framework posters are on the wall. Your team has spent two quarters standing up runtime observability. And sitting in that chair, you still don’t know whether to say yes.
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armo

How to Triage an AI Agent Execution Graph: A Three-Tier Decision Framework for Security Teams

Apr 10, 2026 By Yossi Ben Naim In ARMO
A platform security engineer gets an alert at 2:14 a.m. One of the LangChain agents running in their production Kubernetes cluster has produced an execution graph with eleven nodes, seven tool calls, and an egress edge to a domain that is not in the agent’s approved integration list. The chain is fully rendered in their console. Every signal is there.
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armo

AI Workload Baseline and Drift Detection: Defining "Normal" Agent Behavior

Apr 10, 2026 By Ben Hirschberg In ARMO
Security teams deploying AI agents into Kubernetes know they need behavioral baselines. The concept is straightforward: define what “normal” looks like for each agent, then detect when behavior drifts in ways that suggest compromise. The problem is that AI agents are designed to change. A model update alters inference latency. A prompt revision shifts tool-calling sequences. A new MCP integration adds API destinations nobody flagged during the last security review.
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graylog

What is the OWASP Top 10 for LLM Application Security

Apr 10, 2026 By Jeff Darrington In Graylog
Initially published by the Open Worldwide Application Security Project (OWASP) in 2023, the Top 10 for LLM Application Security list seeks to bridge the gap between traditional application security and the unique threats related to large language models (LLMs). Even where the vulnerabilities listed have the same names, the Top 10 for LLM Application Security focuses on how threat actors can exploit LLMs in new ways and potential remediation strategies that developers can implement.
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Every Tech Revolution Follows This Pattern (AI Is No Different)

Apr 10, 2026 By Razorthorn Security In Razorthorn
AI adoption is happening faster than any technology cycle in history. Information security and risk management are being sacrificed for speed and every single technology revolution has followed the same pattern. In this episode of Razorwire Raw, Jim Rees draws on decades of experience through the internet boom, virtualisation revolution and cloud computing adoption to explain what's actually happening with AI right now. Each cycle has been faster than the last, and each time, security gets left behind.
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Your Convenient AI Agent Is a Backdoor to Your Files #agenticai #promptinjection

Apr 10, 2026 By Razorthorn Security In Razorthorn
People are installing powerful AI agents on everyday laptops without realising those tools can access files, emails and operating system functions. Once prompt injected, that agent can behave like a malicious version of its user, which turns convenience into a direct path for deletion, exfiltration and loss of control.
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salt security

The AI Supply Chain is Actually an API Supply Chain: Lessons from the LiteLLM Breach

Apr 10, 2026 By Eric Schwake In Salt Security
The recent supply chain attack involving Mercor and the LiteLLM vulnerability serves as a massive wake-up call for enterprise security teams. While the security industry has spent the last year fixating on prompt injections and model jailbreaks, this breach highlights a far more systemic vulnerability. The weakest link in enterprise AI is not necessarily the model itself. It is the middleware connecting the models to your data.
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kovrr

AI Governance and Risk: Expert Insights for Enterprise Leaders

Apr 9, 2026 By Kovrr In Kovrr
‍ As GenAI tools become embedded in core business operations, the governance programs meant to oversee them are still catching up. Closing that gap requires visibility into where AI operates and the ability to express exposure in financial terms that leadership can act on. The organizations best positioned to manage AI risk are those that have already started treating it as a measurable business variable rather than an abstract operational concern. ‍
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