Darktrace / NETWORK vs. Darktrace / IDENTITY

Darktrace / NETWORK is a cutting-edge AI-powered platform specifically engineered for network detection and response, with the goal of preventing, identifying, examining, and managing both established and emerging threats in today's digital landscapes. By leveraging its Self-Learning AI, the platform engages directly with an organization’s data, adjusting to the normal behaviors of individual devices, users, connections, and attack methods, thus enabling the detection of anomalies without the dependence on prior attack data, established signatures, or broad models. This solution provides extensive visibility across a range of environments, including on-premises, virtual, cloud, hybrid networks, remote endpoints, operational technology (OT) devices, and zero-trust network access (ZTNA), while also monitoring traffic in both encrypted and decrypted states. In addition, it continuously enhances its detection abilities to improve accuracy and reduce alert fatigue, all without the necessity for manual rule adjustments. The Cyber AI Analyst performs in-depth investigations at scale, generating hypotheses, drawing conclusions, prioritizing incidents based on their potential impact on the business, and correlating events across network, endpoint, cloud, identity, OT, email, and remote systems, which fortifies the defense against cyber threats. Consequently, organizations can achieve a proactive security stance that adapts alongside evolving challenges and threats in the digital world. This adaptive approach ensures that security measures remain effective and relevant in the face of ever-changing threat landscapes.

Darktrace / IDENTITY offers a sophisticated defense solution against identity threats, harnessing the power of AI to combine proactive risk management with immediate detection, in-depth investigation, and self-sufficient responses across diverse IT landscapes. It boasts seamless integration with single sign-on platforms and Active Directory, which provides a thorough view of user interactions by monitoring access and behaviors post-authentication across cloud, SaaS, hybrid, and on-premises environments. By employing Self-Learning AI, the technology conducts millions of calculations using real-time information to determine baseline behaviors for users, devices, applications, service accounts, and other identities, enabling it to identify subtle irregularities that may indicate problems such as account takeovers, credential theft, session-token misuse, insider threats, lateral movement, or data breaches. Furthermore, Peer Group Analysis highlights behaviors that diverge from those of similar users, while Credential Theft Monitoring combines human expertise with the swift analytical power of AI. This comprehensive strategy significantly strengthens the security framework by tackling potential threats proactively, thus preventing them from developing into more severe breaches. Ultimately, the ability to adapt and learn from emerging patterns ensures that organizations remain a step ahead in combating identity-related risks.