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24/06/2026 7:36 pm
TL;DR: Behavioral biometrics adds passive, continuous risk detection to authentication by analyzing typing rhythm, cursor movement, touch pressure, gait, and other interaction patterns, according to 1Kosmos. The security value is not replacement of MFA, but stronger fraud detection and session assurance when identity proofing alone is not enough.
NHIMG editorial — based on content published by 1Kosmos: behavioral biometrics and authentication
By the numbers:
- 90% of IT leaders say properly managing NHIs is essential for a successful zero-trust implementation.
- Only 20% have formal processes for offboarding and revoking API keys, and even fewer have procedures for rotating them.
Questions worth separating out
Q: How should security teams use behavioral biometrics in authentication flows?
A: Use behavioral biometrics as a continuous risk signal inside an authentication flow, not as a standalone proof of identity.
Q: When do behavioral biometrics create more risk than they reduce?
A: They create more risk when teams treat them as a universal trust score or use them without clear thresholds and exception handling.
Q: What do organisations get wrong about continuous authentication?
A: They often assume continuous authentication means constant surveillance that can replace stronger identity controls.
Practitioner guidance
- Define where behavioral signals are allowed to influence access Limit behavioral biometrics to step-up decisions, session assurance, and fraud scoring for named use cases such as high-risk transactions or anomalous login behavior.
- Set thresholds for false positives and false negatives Measure how often legitimate users are challenged and how often suspicious behavior is missed, then tune the model against real business outcomes.
- Tie behavioral monitoring to explicit incident playbooks Define what happens when a session drifts outside normal behavior, including when to require reauthentication, terminate the session, or trigger fraud review.
What's in the full article
1Kosmos's full article covers the implementation detail this post intentionally leaves for the source:
- Behavioral data collection methods for typing, cursor movement, touch, and gait patterns
- The three-stage collection, analysis, and decision workflow used in behavioral authentication
- Operational guidance on balancing false acceptance and false rejection rates
- Examples of how behavioral signals integrate with broader passwordless and MFA deployments
👉 Read 1Kosmos's full guide to behavioral biometrics and authentication →
Behavioral biometrics and authentication risk: what IAM teams need?
Explore further
25/06/2026 4:57 am
Behavioral biometrics is an assurance layer, not an identity foundation. The control improves confidence that the same user remains present, but it does not solve credential lifecycle, privileged access, or third-party delegation. That distinction matters because IAM programmes often over-assign meaning to any signal that reduces friction. The practitioner conclusion is simple: treat behavioral biometrics as a session-risk input, not as a substitute for account governance.
A few things that frame the scale:
- Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
- 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools.
A question worth separating out:
Q: How can teams evaluate whether behavioral biometrics are working?
A: Measure whether the control reduces fraud and account takeover without creating excessive user friction. Good indicators include fewer suspicious sessions reaching sensitive actions, stable challenge rates for legitimate users, and clear analyst visibility into why a session was escalated. If those metrics are not available, the program is not mature enough for broad rollout.
👉 Read our full editorial: Behavioral biometrics are reshaping modern authentication risk
