What is Identity Analytics?

With the rapid digital transformation of businesses and the shift to remote workforces, identity and access management has become more complex than ever. Organizations are seeing an increasing number of identities across diverse systems and applications that need to be managed and secured. This has made traditional identity management practices insufficient.



Identity Analytics provides a innovative approach to address these modern identity challenges. At its core, identity analytics leverages machine learning and big data techniques to analyze identity and access data across an organization. It aims to gain valuable insights into identity risks, entitlements, anomalies and optimization opportunities. With these insights, organizations can enhance identity governance, streamline access management processes and strengthen overall digital identity security.



Identities are More Scattered than Ever

Today's digital identities are scattered across numerous systems - from cloud applications to on-premise directories. With remote and hybrid work models, the number of connections from unmanaged devices has also increased drastically. This has resulted in identities that are not centralized anymore and difficult to monitor with traditional tools.



Identity analytics collects identity data from various sources like Active Directory, cloud applications, endpoints etc. Using advanced correlations, it maps relationships between diverse identities, accounts, entitlements and systems. This provides a unified view into all digital identities within the organization from a single dashboard. With its ability to analyze identities at an unprecedented scale, identity analytics delivers vital insights into managing modern, distributed identity ecosystems.



Detecting Identity and Access Risks

One of the key capabilities of identity analytics is continuous monitoring of identities for risk detection. By analyzing identity behavior patterns and entitlement usage over time, it can detect anomalous access, excessive privileges, dormant accounts and insider threats. Identity analytics leverages machine learning algorithms to benchmarks "normal" behavior and automatically surfaces any deviations for further investigation.



Some examples include - discovering accounts being accessed from unusual locations/IP addresses, spotting entitled user groups not aligned with job functions, finding shared or generic accounts with excessive permissions and flagging terminated employees retaining access to critical systems. With such risks proactively identified, organizations can take timely actions to mitigate them and strengthen their overall identity security posture.



Optimizing Access Management Processes

To keep up with business needs, organizations need to constantly evaluate and adjust user access entitlements. However, manual identity reviews at scale are time-intensive and error-prone. Identity analytics leverages its insights into real-time and historical identity data and access patterns to recommend optimization opportunities.



For instance, it can analyze which user groups or roles actually require access to specific applications/systems based on usage patterns. Or detect entitlements that can be revoked as job functions change over time without manual reviews. Identity analytics continuously monitors the principle of least privilege and points out where access rights can be reduced without impacting productivity. This helps automate access certifications and ensure entitlements are optimized on an ongoing basis.



Enhancing Identity Governance Compliance

Complying with various regulatory mandates like GDPR, CCPA, PCI DSS around user access and consent is challenging without the right visibility. Identity analytics collects and synthesizes identity data into interactive reports and dashboards to simplify audits and demonstrate compliance. It helps answer questions like - which users have access to regulated customer data sets? are entitlements aligned with data security policies? etc.



Additionally, identity analytics can be leveraged to automate identity lifecycle workflows required as part of governance programs. For instance, triggering automated access revocation/removal 30 days after an employee leaves. Or automatically suspending accounts of contractors during non-working hours based on policy. Such workflow automation powered by identity analytics insights ensures identity governance compliance with less manual effort.



In through its ability to analyze and draw correlations from massive volumes of distributed identity data, identity analytics has emerged as a powerful means for organizations to enhance identity security, streamline access management and automate compliance in today's complex digital ecosystem. By gaining deeper visibility into user identities and behaviors, companies can proactively detect risks, optimize access privileges on an ongoing basis and strengthen overall identity governance. As threats evolve, identity analytics will continue playing a crucial role for organizations to protect their most critical digital assets - user identities.

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About Author:

Ravina Pandya, Content Writer, has a strong foothold in the market research industry. She specializes in writing well-researched articles from different industries, including food and beverages, information and technology, healthcare, chemical and materials, etc. (https://www.linkedin.com/in/ravina-pandya-1a3984191)