Find out what your peers are saying about Microsoft, Dtex Systems, Safetica and others in Insider Risk Management.
| Product | Market Share (%) |
|---|---|
| Microsoft Purview Insider Risk Management | 20.1% |
| Proofpoint Insider Threat Management | 19.8% |
| Dtex Systems | 13.7% |
| Other | 46.39999999999999% |
| Product | Market Share (%) |
|---|---|
| Splunk User Behavior Analytics | 8.9% |
| Exabeam | 9.6% |
| IBM Security QRadar | 9.3% |
| Other | 72.2% |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Midsize Enterprise | 5 |
| Large Enterprise | 12 |
Microsoft Purview Insider Risk Management helps organizations identify and manage potential internal threats by utilizing advanced analytics and insights to minimize risk.
With a focus on addressing internal threats, Microsoft Purview Insider Risk Management employs sophisticated analytics to proactively detect and manage risks. It offers context-rich insights to protect data, helping businesses maintain compliance and safeguard their information. By implementing mechanisms to predict potential risks, it aids in preventing data loss and ensures that sensitive information remains secure.
What are the most important features of Microsoft Purview Insider Risk Management?In industries like finance and healthcare, where data sensitivity is critical, adopting Microsoft Purview Insider Risk Management can be crucial. For example, financial institutions utilize this tool to detect and mitigate fraudulent activities, while healthcare providers leverage its capabilities to protect patient data, ensuring compliance with data protection regulations. Its implementation varies across industries but consistently focuses on securing valuable information and reducing risk exposure.
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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