Find out in this report how the two Identity Threat Detection and Response (ITDR) solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
Generally, the support is more effective than other providers like Oracle.
One improvement I would recommend is the integration of an admin application within Teams, allowing easy access to attack information on a mobile platform.
Ensuring a fair price according to market standards.
The most valuable feature is its hybrid artificial intelligence, which gathers forensic data to track and counteract security threats, much like the CSI series in effect.
Microsoft Defender for Identity integrates with Microsoft tools to monitor user activity, providing advanced threat detection and analysis using AI. It enhances proactive threat response and security visibility, making it essential for securing on-premises and cloud environments like Active Directory.
Microsoft Defender for Identity offers comprehensive monitoring and AI-driven user behavior analysis. It detects threats through real-time alerts and identifies lateral movements and entity tagging, ensuring robust security management. With excellent visibility via its dashboard, it supports customized detection rules and seamlessly integrates with SIEM platforms. While SecureScore and SecureScan provide robust environment security, there is room for improvement in cloud security, on-premises application integration, and remediation capabilities. Azure integration is limited, and the administrative interface could be more user-friendly. Users experience frequent false positives, affecting threat detection efficiency.
What key features stand out in Microsoft Defender for Identity?In specific industries such as education and finance, Microsoft Defender for Identity is crucial for securing on-premises Active Directory and Azure Active Directory environments. It effectively detects suspicious activities and manages conditional access policies, offering user and entity behavior analytics, endpoint detection and response capabilities. This helps prevent unauthorized access and strengthens overall security, making it an invaluable asset for organizations aiming to safeguard their digital infrastructure.
Splunk User Behavior Analytics is a behavior-based threat detection is based on machine learning methodologies that require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics for users, devices, service accounts and applications. It detects insider threats and external attacks using out-of-the-box purpose-built that helps organizations find known, unknown and hidden threats, but extensible unsupervised machine learning (ML) algorithms, provides context around the threat via ML driven anomaly correlation and visual mapping of stitched anomalies over various phases of the attack lifecycle (Kill-Chain View). It uses a data science driven approach that produces actionable results with risk ratings and supporting evidence that increases SOC efficiency and supports bi-directional integration with Splunk Enterprise for data ingestion and correlation and with Splunk Enterprise Security for incident scoping, workflow management and automated response. The result is automated, accurate threat and anomaly detection.
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