Security Information and Event Management (SIEM) has long been an important component of cybersecurity operations. Traditional SIEM platforms collect logs and security events from endpoints, servers, applications, networks, cloud services, identity systems, and other infrastructure, then provide centralized analysis and alerting. However, modern IT environments generate enormous volumes of security telemetry. Cloud adoption, remote work,
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Security operations teams generate and investigate thousands of alerts from endpoints, networks, applications, cloud environments, identities, and security tools. The problem is that not every alert represents a genuine cyberattack. Many are false positives caused by legitimate user activity, configuration changes, unusual but harmless behavior, incomplete threat context, or overly sensitive detection rules. A high
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Identity has always been central to cybersecurity. Organizations traditionally focused on protecting human identities such as employees, administrators, contractors, customers, and partners. Authentication systems, passwords, multifactor authentication, privileged access management, and single sign-on were primarily designed around people. That model is changing. Modern organizations now operate thousands or millions of machine identities representing applications, APIs,
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AI agents are moving from simple conversational interfaces to systems capable of performing multi-step tasks. An agent may retrieve information, access databases, call APIs, execute workflows, interact with cloud services, send messages, or make decisions with limited human intervention. That autonomy creates a security challenge that traditional application monitoring does not completely address. A malicious
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