Cyberattacks are becoming more persistent, automated, and difficult to manage with traditional security tools alone. Organizations may have firewalls, endpoint protection, vulnerability scanners, identity security, cloud security, and other technologies in place, yet still struggle to determine which alerts represent genuine threats and what actions should be taken. This challenge is particularly significant for organizations
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Your organization is already running AI you can’t see. Employees are pasting contracts, patient records, and source code into public chatbots. Developers have wired large language models (LLMs) into production. Autonomous agents are taking actions on live systems with no human in the loop. Every one of those is an open door – and here’s
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August 2026 produced one of the more punishing monthly breach recaps in recent memory: nine major incidents, six countries across five continents, and ransomware crews named Everest, Chaos, and a two month old outfit calling itself xpl0itrs, all claiming victims within the same few weeks. A national tax authority and a national justice ministry were
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Security teams have traditionally relied on Security Information and Event Management (SIEM) platforms to collect logs, correlate security events, investigate suspicious activity, and support incident response. As enterprise environments have become more distributed, the amount of security telemetry has grown significantly. Organizations now generate data from endpoints, networks, cloud infrastructure, applications, identity systems, SaaS platforms,
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