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.
The solution can save costs by improving incident resolution times and reducing security incident costs.
Generally, the support is more effective than other providers like Oracle.
The quality of support is very good, but troubleshooting can take time due to complex setups and the need to provide many logs.
Mission-critical offering a dedicated team, proactive monitoring, and fast resolution.
From the responsiveness perspective, Splunk is very responsive with SLA-bound support for premium tiers.
I would rate the support at eight, meaning there's some room for improvement.
In a Microsoft-centric organization, especially with Azure infrastructure and Office 365, Microsoft Defender for Identity is scalable.
Splunk User Behavior Analytics is highly scalable, designed for enterprise scalability, allowing expansion of data ingestion, indexing, and search capabilities as log volumes grow.
Microsoft Defender for Identity is quite robust and built on Azure hyperscale infrastructure, with a 99% availability.
We do not see any issues with the stability of Microsoft Defender for Identity.
Having recently started using it, reliability is affirmed, but manual investigation is often performed to verify if alerts identified by auto-remediation are accurate.
With built-in redundancy across zones and regions, 99.9% uptime is achievable.
Splunk User Behavior Analytics is a one hundred percent stable solution.
Splunk User Behavior Analytics is highly stable and reliable, even in large-scale enterprise environments with high log injection rates.
If Microsoft could develop a feature that indicates when impossible travel is caused by VPN connections, it would prevent unnecessary password resets and session disruptions, especially for VIP users in organizations.
One improvement I would recommend is the integration of an admin application within Teams, allowing easy access to attack information on a mobile platform.
Reducing false positives is something we've been working on with Microsoft.
Global reach allows deployment of apps and services closer to users worldwide, but data sovereignty concerns exist and region selection must align with compliance requirements.
I encountered several issues while trying to create solutions for this advanced version, which seem unrelated to query or data issues.
High data ingestion costs can be an issue, especially for large enterprises, as Splunk charges based on the amount of data processed.
If they can reduce the costs, organizations will be happy, and it will compensate for using the Azure environment, which is more expensive on the infrastructure as a service side.
Ensuring a fair price according to market standards.
From an organization perspective, using E5 licenses is value for money, especially if Azure and Office 365 are already in use.
Reserved instances with one or three-year commitments offer lower rates, providing up to 70% savings.
Comparing with the competitors, it's a bit expensive.
The pricing is based on the amount of data processed, and it is considered a high-level investment for enterprises.
We receive an advance report of risky users, allowing us to take preemptive action before an attack causes damage to organization details.
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.
The advanced threat protection is one of the strengths of Microsoft Defender for Identity, as it utilizes user and entity analytics and can detect indicative attacks.
I also utilize it for anomaly detection and behavior analysis, particularly using Splunk's machine learning environment.
Features like alerts and auto report generation are valuable.
Splunk User Behavior Analytics offers several beneficial features, such as Insider Threat Detection, account compromise detection, risk scoring, threat detection, and machine anomaly detection.
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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