Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

netwrix

Top AI cybersecurity companies in 2026

Apr 11, 2026 By Netwrix Team In Netwrix
AI cybersecurity companies in 2026 fall into two categories: platforms using AI to automate detection, investigation, and response, and platforms built to secure the AI systems organizations are now deploying. With this grouping into ‘AI for Security’ and ‘Security for AI’, this article covers the breadth and depth of AI cyber security companies.
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Lightboard series - Secure your AI-powered applications with Cloudflare

Apr 11, 2026 By Cloudflare In Cloudflare
Humair from Cloudflare walks through the details of how Cloudflare's AI Security for Apps secures AI-powered applications. Learn how Cloudflare can discover AI/LLM endpoints and detect and mitigate AI-specific threats like PII exposure, unsafe/toxic content, prompt injection and jailbreak. Learn more.
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datadog

Introducing our open source AI-native SAST

Apr 10, 2026 By Julien Delange In Datadog
Static application security testing (SAST) tools help developers quickly catch potential vulnerabilities as they code. However, these tools rely on inflexible rules that often generate a high number of false positives, reducing trust in their accuracy and slowing adoption. To help developers access context-aware vulnerability detection, we’ve released an open source AI-native SAST solution. This tool scans code changes incrementally and surfaces security issues in real time.
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one identity

How AI is changing IGA

Apr 10, 2026 By Robert Kraczek In One Identity
It’s no surprise that AI is being integrated into identity governance and administration (IGA) platforms. Automation promises productivity boosts, risk detection can be in real-time and cloud environments allow greater scalability. What’s more, the pace of AI means IGA is quickly moving beyond slower, more rigid, rule-based approaches.
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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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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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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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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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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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