Teams should join authentication events to journey analytics so they can see where users abandon sign-in, where controls create friction, and where risk signals are genuinely suspicious. That allows product, digital, and security teams to tune policies together instead of chasing separate metrics. The result is better assurance with fewer avoidable drop-offs.
Why This Matters for Security Teams
Login telemetry is one of the few data streams that can show both security friction and customer friction in the same event trail. When teams only measure failed logins, they miss the difference between a blocked attack, a mistyped password, and a legitimate user abandoning sign-in. Mature programs connect authentication events to journey analytics and risk scoring, then use those signals to tune controls rather than simply tighten them.
That matters because identity is now a customer-pathway issue, not just an access-control issue. The NIST Cybersecurity Framework 2.0 treats identity and access as core governance and protection outcomes, while NHIMG research shows how weak monitoring and logging remains a leading contributor to identity compromise in the real world. In the State of Non-Human Identity Security, inadequate monitoring and logging is cited as a cause of NHI-related attacks by 37% of organisations, which is a useful reminder that visibility gaps often outlast policy design.
In practice, many security teams discover that login controls are breaking conversion only after support tickets and abandoned sessions have already increased.
How It Works in Practice
Useful login telemetry starts with event quality. Capture the full authentication sequence, not just success or failure: username entry, MFA challenge issued, MFA completion, password reset, device trust, geo anomalies, session creation, and recovery-flow exits. Then join those events to product analytics so teams can see where users stall, retry, or drop off. Security teams should classify each event by intent and risk, while product teams use the same telemetry to identify avoidable friction.
A practical model is to segment logins into three buckets. First, low-risk, low-friction journeys that should move quickly with minimal prompts. Second, ambiguous journeys that need step-up checks such as one-time verification or stronger device binding. Third, high-risk journeys that justify stronger controls, additional review, or temporary denial. This is where policy-as-code and runtime evaluation matter. Current guidance suggests that static rules alone are too blunt for modern identity flows, especially when customer traffic patterns change by device, region, and channel.
The most effective teams also normalize identity telemetry across channels. Web, mobile, API, and partner login flows should be measured with shared success metrics so the organisation can answer questions like: which step causes the highest abandonment, which challenge produces the most false positives, and which risky signal most often predicts a real compromise?
- Use consistent event names and timestamps across auth systems.
- Join auth logs with session analytics, support contacts, and recovery outcomes.
- Track challenge rate, abandonment rate, false positive rate, and reset completion.
- Review step-up prompts after feature launches, geo expansion, or device-policy changes.
The State of Non-Human Identity Security also shows that visibility is often partial at best, which is why logging must be designed for investigation and tuning, not just retention. These controls tend to break down in federated, multi-brand environments because inconsistent identity flows make it hard to compare user journeys across systems.
Common Variations and Edge Cases
Tighter login controls often increase assurance, but they also raise support load and abandonment risk, so organisations have to balance fraud prevention against customer friction. Best practice is evolving here, and there is no universal standard for how much step-up is acceptable in every channel.
One common edge case is returning users on trusted devices. Over-challenging these sessions can create unnecessary drop-off, while under-challenging them can hide credential theft. Another is passwordless adoption, where telemetry must shift from password failure analysis to device enrollment, authenticator reliability, and recovery path quality. A third is bot-driven traffic, where login telemetry can be polluted by automated retries that look like human frustration unless the data is segmented carefully.
Security and CX teams should also watch for policy drift. If risk thresholds are tuned only to reduce abandonment, attackers may find softer paths through recovery flows or partner federations. If thresholds are tuned only to block suspicious activity, legitimate users may be forced into repeated verification loops. The better pattern is to review telemetry alongside incident outcomes and product releases, then recalibrate controls at the same cadence as customer journeys.
NHIMG guidance on identity resilience, including the Schneider Electric credentials breach, reinforces a practical lesson: auth telemetry is most valuable when it is used to find both compromise paths and avoidable user friction before they become operational problems.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.RM-03 | Login telemetry supports risk decisions and control tuning across security and CX. |
| OWASP Non-Human Identity Top 10 | NHI-09 | Monitoring and logging are central to spotting compromised identities and bad sign-in patterns. |
| NIST AI RMF | The AI RMF is relevant where analytics or scoring shape step-up authentication decisions. |
Govern telemetry-driven scoring so step-up decisions are explainable, tested, and periodically reviewed.
