Advancements in AI and GRC Product Flexibility a Key Market Differentiator
Mitratech, a leading global provider of legal, compliance, and human resources (HR) software, is excited to announce that it has been recognized again by SPARK Matrix’s Competitive Landscape Analysis Report as a leader in the governance, risk, and compliance technology space.
“Mitratech’s AI-powered GRC platform, Alyne, offers various modules covering risk management and compliance areas,” Senior Analyst at Quadrant Knowledge Solutions, Siddarth Kumar, explained. “With its comprehensive GRC capabilities, strong customer value proposition, product strategy, and robust roadmap, Mitratech has received a strong rating across the parameters of technology excellence and customer impact, positioning them as technology leaders in the SPARK Matrix: Governance, Risk, and Compliance (GRC) Platforms in 2023.”
Quadrant Knowledge Solutions’ SPARK Matrix report takes market dynamics, major trends, and competitive positioning into consideration when hand-selecting the emerging platforms currently shaping the future of GRC and for the second year in a row, Mitratech’s AI-driven enhancements and comprehensive Enterprise Risk Management (ERM) capabilities have landed the organization coveted recognition as a leader in the field.
“We have led the market by advancing generative AI summarization for the last 18 months,” said Mike Williams, CEO of Mitratech. “Our customers have been benefiting from this technology in our products ever since, and it’s exciting to see the industry accelerating and operationalizing key use cases. We’ve also made a concerted effort to increase the agility and time-to-value that our products deliver. We will always strive to lead with innovation and are thrilled to once again receive the recognition as a GRC technology leader in this report.”
Quadrant Knowledge Solutions expanded further on some notable examples of Mitratech’s AI-driven capabilities and enhancements, which include:
- Document and evidence summarization: automatically understands documents and summarizes the content, providing users with a general assessment of the suitability of the associated document as evidence
- Control mapping: enables machine learning (ML) models to suggest the most relevant controls to a given text for control mapping, control mitigation suggestions, and determining the relevance of the evidence to the given assessment question
- Library risk mapping: proposes potential risks for a Control
- Managed risk mapping: assists the end user in creating duplicate risks in the risk creation process by finding semantically similar, existing risks in the risk creation screen