Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Torq

From Intent to Outcome: How Agentic Coding is Transforming the SOC

Mar 18, 2026 By Ofer Smadari In Torq
See how Torq harnesses AI in your SOC to detect, prioritize, and respond to threats faster. Request a Demo Security teams are being asked to move faster and handle more complexity, while the threats they defend against are increasingly AI-assisted. When I wrote about VoidLink in January, my point was simple: you cannot fight machine-speed threats with human-speed defense. Attackers are using AI to code, adapt, and scale attacks while humans are still grinding away doing the heavy lifting in the SOC.
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Rethinking Application Delivery for the AI Era

Mar 18, 2026 By A10 Networks In A10 Networks
Rethinking Application Delivery for the AI Era Is your network strategy keeping up with the AI era? Jamison Utter, Field CISO at A10 Networks, challenges IT leaders to move beyond "piecemeal" infrastructure and rethink their approach to application delivery. As organizations face the dual pressure of integrating AI workloads and managing a vast "fleet" of hybrid devices, the old ways of operating are becoming a liability. Jamison discusses the true cost of administrative overhead and the urgent need for a more flexible, simple, and future-proof vendor strategy.
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Secure Enterprise AI Apps and Agents: Visibility, Governance, Runtime Protection

Mar 18, 2026 By Cato Networks In Cato Networks
When you deploy an AI application, do you know what's being sent into it — or what's coming back out? Cato AI Security provides runtime protection for the AI applications your organization builds and deploys, with real-time enforcement, sensitive data anonymization, and a complete audit trail across every interaction. Learn more or request a demo at catonetworks.com.
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11:11 systems

Unlock AI with GPU as a Service in VCF 9

Mar 18, 2026 By Justin Giardina In 11:11 Systems
Many IT professionals struggle to integrate artificial intelligence (AI) into their existing environments. You often find expensive hardware trapped in isolated clusters or dedicated hosts. Your infrastructure team manages access through manual ticket queues, which leads to low utilization and frustrating bottlenecks for developers. When you don’t have a standardized way to share and monitor accelerator resources, every hardware change risks downtime for your critical applications.
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jfrog

Survive the AI Code Blizzard: Introducing Snippet Detection

Mar 18, 2026 By Dafna Zahger Bernanka In JFrog
In 2026, software development speed is an AI-solved problem. Yet, as AI-generated code volumes surge, organizations face a new kind of risk visibility gap. Developers are increasingly copying third-party snippets into their codebases—from both AI prompts and open-source software components—creating large security and compliance blind spots that lead to significant risks.
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acronis

RMM AI tools: Choosing AI-powered RMM software for MSPs and IT teams

Mar 18, 2026 By Acronis In Acronis
Modern managed service providers (MSPs) are increasingly adopting RMM AI tools — remote monitoring and management software enhanced with artificial intelligence — to keep pace with growing IT demands. Traditional RMM platforms allow MSPs to remotely monitor client endpoints, deploy patches, run scripts and troubleshoot issues from a central console. Now, AI-powered RMM software is taking this a step further.
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protecto

What Is Format-Preserving Encryption (FPE)?

Mar 18, 2026 By Sakshi In Protecto
Your database stores a credit card number: 4532 1234 5678 9010. You encrypt it for security. Now it looks like this: %Xk92@!mQz#Lp&7. Problem. Your payment system can’t process that. It expects a 16-digit number. Your billing software breaks. Your downstream analytics fail. Your whole pipeline comes to a halt. This is the exact problem that format-preserving encryption was built to solve.
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AI Risk Isn't Just About Models. It's About Systems.

AI Risk Isn't Just About Models. It's About Systems.

Mar 18, 2026 By SecuritySenses In SecuritySenses
Most discussions about AI risk focus on the models themselves. Hallucinations. Bias. Data leakage. Unpredictable outputs. These are real concerns. But they only tell part of the story. Because in practice, AI doesn't operate in isolation. It operates inside systems - and that's where the real risk begins to emerge.
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