Machine Learning for Identity Access Management
Identity and Access Management is constantly evolving across critical functions including data security, authentication, synchronizing internal data, enabling consumer contact preference management and meeting privacy compliance requirements to name a few.
According to Forrester Research’s Report, Top Trends Facing Shaping IAM in 2019, January 3, 2019 “Protecting the enterprise from data breaches, malicious insiders and fraud remains a top business priority and is driving demand for smarter identity analytics that can dynamically detect anomalous user activity.” simMachines similarity based explainable AI (XAI) technology is a perfect fit for this challenge given its ability to explain anomalous behavior.
Similarity Based Explainable AI (XAI) Provides Actionable Intelligence for IAM Analytics
Explainable machine learning is critical for not only detecting security risks, but also in explaining what the key factors are in order to understand and prevent them from occurring
simMachines Provides Unparalleled Transparency Into AI Predictions
High precision with local prediction transparency
Single pass prediction and clustering function
Easy to use, with advanced capabilities for Data Scientists
Dynamically weighted factors by prediction
Used controlled cluster granularity
Predictive factors easily integrate into decision systems
Identity Access Management Requires Explainable Threat Detection and Constant Monitoring Through Automated Insights
Review & Investigation
Explainable Detection
Patterns & Trends
Know What Your Machine Knows
Identity access management professionals need machine learning applications that provide full explainability for each and every prediction, to understand access breach causes and their defining factors, enable investigation and review, detect new emerging patterns and constantly monitor changes and differences in identity theft and breach methods. simMachines XAI technology leverages proprietary algorithms to provide precise predictions and automated insights at speed and scale to accomplish these goals.
One Class Learner for Explainable Anomaly Alerts
Machine Driven Explainable IAM Predictions
Dynamic Predictive Clustering for IAM Insights
Anomalous Pattern Detection, Trending & Analysis
Flexible Deployment Options Including Restful APIs
Access to Data Scientists and Expert Customer Support
Use cases
simMachines supports a variety of use cases around Identity and Access Management.
Probabilistic Identity Matching
CDP and DMP wanted to improve deterministic match rates to improve lookalike model performance and increase ad reach for their clients.
Adaptive Authentication
Our partner needed an explainable anomaly detection capability to expand beyond its rule-based system to detect unauthorized access to its network.
Device Linking
Large DMP needed to improve linking of multiple devices from anonymous users.
Access Rights & Reconciliation
Our partner needed to be able to group together application access rights by role for its clients to assess existing access rights in terms of uniformity as well as assign the right level of access to new employees.
Recent news in IdentityALL NEWS ON OUR BLOG
Artificial intelligence and machine learning are rapidly on the rise. Having just attended Forrester’s Data Strategy and Insights Conference in Orlando, FL, we heard these terms frequently mentioned in many presentations.
*The Insights-Driven Business, July 2016, Forrester Research
