About Autonomous Access
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Identity Cloud add-on capability
Contact your ForgeRock representative if you are interested in adding ForgeRock® Autonomous Access to your Identity Cloud subscription. Refer to Add-on capabilities. |
Autonomous Access leverages artificial intelligence (AI) and machine learning (ML) techniques to analyze threat signals and anomalous behavior patterns. It provides an AI-powered threat detection solution to prevent account takeover and fraud at the identity perimeter.
Autonomous Access speeds and simplifies access decisions, letting your organization block threats and deliver personalized journeys that enhance the digital experience of legitimate users.
ForgeRock deploys Autonomous Access in your new of existing development, staging, and production Identity Cloud tenants. Your customers' data and any personally identifiable information are never accessible outside the tenant.
Activity dashboard
The Activity dashboard shows risky access activity and lets users drill in and investigate across time, risk reason, and risk score in the realm that you are currently in (for example, Alpha, Bravo, or Root).
Autonomous Access nodes
Autonomous Access provides three prebuilt nodes and a template for journeys. No custom coding or connectors are required to implement these journeys. With the addition of the three nodes, you can take full advantage of the 100+ nodes to trigger actionable outcomes when high risk scores are discovered.
Risk scores are a combination of anomalous behavior and discovered threats. You can determine what outcomes to take for certain risk score ranges. For example, when Autonomous Access returns lower risk scores (for example, 0–30), you can configure Autonomous Access to allow a user to pass without further MFA. For higher risk scores (for example, 71–100), you can configure Autonomous Access to flag these events for escalation, such as step-up authentication, MFA, block, review, inform user, or other actions.
The following nodes are available:
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Autonomous Access signal node: Assesses risk based on anomalous user behavior, credential stuffing, suspicious IPs, automated user agents (bots), impossible travelers, and brute force attacks using AI/ML analytics. The result is a risk score from 0 (no risk) to 100 (high risk).
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Autonomous Access decision node: Maps the risk score to a high, medium, low, or unknown branch of a journey to direct the user experience.
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Autonomous Access results node: Sends data back to Autonomous Access for the dashboards and model learning.
Figure 2. Autonomous Access nodes