Updated: Oct 30
In today's digital landscape, identity has become the new perimeter, yet traditional security measures struggle to keep pace with evolving threats. An essential component of identity observability, time-based analysis, is emerging as a critical dimension in Identity Security Posture Management (ISPM), offering unprecedented insights into the dynamic nature of identity risks.
In this blog post, we'll explore how adding a temporal context into the identity observability and ISPM efforts can transform an organization's ability to detect, prevent, and respond to identity-based threats and enable them to better navigate the complex and fragmented identity infrastructure.
New Challenges in Maintaining a Robust Identity Security Posture
Identity infrastructure in most organizations is complex and fragmented, too often making it fragile and vulnerable to attacks. The lack of continuous comprehensive visibility hampers the ability to effectively assess and manage the security posture, making it difficult to identify and address identity hygiene issues that could lead to significant security risks. Especially when it comes to dynamic enterprise environments.
In order to protect a complex identity fabric against threats and improve an overall identity security posture, it is essential to have tools for continuous monitoring of all identity activity and access paths, human and non-human, across any cloud or platform.
We at AuthMind talk to identity management and security professionals every day about the challenges related to improving their identity security posture. Some of the most cited include:
- Identity Sprawl and Complexity: controlling identity sprawl, complex configurations, access without MFA, and more while enabling business operations
- Revealing identity blind spots: dormant accounts and bypasses of security controls
- Contextualizing risky identity activities: Detecting risky identity activities, like unauthorized access, or bypasses of security controls with full context and visibility across the entire identity infrastructure.
The Importance of Time-Based Context to Identity Observability
None of these challenges can be effectively solved without comprehensive identity observability, enabling security professionals to collect and quickly explore identity activities and access paths, and interpret any uncovered suspicious patterns.
Time is a crucial dimension for effective identity observability. Static snapshots of identity security are no longer sufficient. Historical context and dynamic time-based analysis unlock extensive operational value of identity observability, significantly reducing time-to-value in addressing identity risks.
Time-based contextualization enhances incident response, allowing teams to retrospectively analyze identity activity for both human and non-human identities. This approach also strengthens identity security posture management by enabling monitoring of posture changes, trends, and outliers over time, like fluctuations in identity creation. It enables identity and security teams to drill down into specific time frames with adjustable granularity, offering deep insights into incidents, trends, and patterns across the identity infrastructure.
Use Cases for Time-Based Identity Observability
Time-based analysis unlocks multiple high-value use cases, enhancing an organization's ability to manage and secure identities effectively. These include:
- Enhanced identity visibility: detect all new identities that became active in the previous week, or all identities, assets, or identity systems inactive for a specific period of time.
- Incident response: interpret and investigate identity activity for both human and non-human identities with precision and granularity.
- Improving identity hygiene and security posture management: analyze identity posture trends, issues related to authentication or configurations, like identifying service accounts with high authentication failure rates, or unveiling failure rates over a specified time frame across different identity systems to pinpoint potential misconfigured systems or identities.
- Risky activity and potential compromise detection: uncover credentials misuse or shared credential usage through conflicting access locations during the specific time frame.
AuthMind: Introducing Time-Based Identity Analysis
Acknowledging the significance of in-depth historical context for identity activities, AuthMind has recently introduced Time-Based Analysis, a powerful new feature to its Identity Security Platform. This enhancement allows customers to analyze and research identity activities at any point in time, both across the environment and within specific incidents.
By integrating dynamic time-based analysis, AuthMind now allows users to dive into select preset time frames (e.g., last 24 hours, past week), custom date/time ranges, or specific periods based on the spikes and outliers on the trend histogram of access activities. The platform makes it easy to conduct complex extensive investigations across high-activity intervals or anomalous events with precision while seamlessly switching the analysis between granular insights and broad views of patterns and trends.
New time filtering capabilities of the AuthMind Platform, including preset and custom ranges.
See AuthMind in Action
With these innovative enhancements, AuthMind takes another important step in enabling its customers to:
- Ensure robust identity hygiene with continuous identity observability across the identity infrastructure, including all identities (human and non-human) accessing cloud, SaaS, and on-premises assets.
- Uncover and eliminate identity blind spots such as dormant service accounts, shadow SaaS usage, or users circumventing security controls to access applications and systems
- Detect and mitigate any risky identity activity, including bypasses of security controls, privilege access tools, or suspicious activity to protect the infrastructure against the identity-based threats.
Request a personalized demo to learn how AuthMind can help address your specific identity security needs.