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How does ForgeRock AM support Adaptive Authentication?

Adaptive authentication (also called risk-based authentication) in ForgeRock AM is the practice of adjusting the authentication challenge based on real-time contextual risk signals. Instead of requiring every user to pass a fixed MFA challenge, AM evaluates risk factors and demands a stronger proof of identity only when the risk profile warrants it. This balances security with user experience — low-risk logins get a frictionless single-factor experience; high-risk logins are challenged with MFA.

Risk signals that AM can evaluate natively include:

  • Device Fingerprint — Collects browser characteristics (user agent, screen resolution, timezone, plugins) and compares against a stored fingerprint for that user. A new or unrecognised device triggers a challenge.
  • IP Address / Geolocation — AM can check the originating IP against a blocklist, whitelist, or GeoIP database. Logins from unexpected countries or IP ranges increase the risk score.
  • Previous Session Data — Comparing current login time/location against historical patterns.
  • Authentication Level — The tree can check whether the current session already has a sufficient auth level before demanding re-authentication.
  • Scripted Risk Scores — Custom Scripted Decision Nodes can call external fraud/risk APIs (such as ForgeRock Intelligent Access, ThreatMetrix, or in-house systems) and route based on the returned score.

A typical adaptive tree flow:

  1. Collect username and password → Data Store Decision node.
  2. Collect device fingerprint → compare against stored fingerprint.
  3. If fingerprint matches → check IP reputation via scripted node.
  4. If IP is clean and fingerprint matches → grant access (single factor).
  5. If device is new or IP is suspicious → route to TOTP or Push MFA node.
  6. If high risk (blocked IP, impossible travel) → deny or require additional verification.
What does the Device Fingerprint node in ForgeRock AM adaptive authentication compare?
How can ForgeRock AM incorporate an external fraud scoring API into an adaptive authentication tree?

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