AI Workflow for Frictionless Single Sign-On (SSO) with Invisible Security

By replacing static, time-based MFA prompts with dynamic, risk-based authentication, legitimate users experience fewer interruptions. This reduces the cognitive load and time spent on authentication, directly translating to higher throughput for knowledge workers and improved satisfaction scores, especially for remote and mobile teams.

Automating the diagnosis of access issues and providing guided self-remediation cuts helpdesk ticket volume for MFA resets and 'access denied' errors. The workflow's intelligent logging also accelerates root-cause analysis for security and operations teams, reducing mean time to resolution (MTTR) for access-related incidents.

Continuous behavioral biometrics and device health verification create a moving target for attackers, making stolen credentials less useful. Automated session termination for high-risk anomalies contains potential breaches faster than manual monitoring. This demonstrably reduces the attack surface and supports compliance with frameworks requiring adaptive access controls.

The workflow generates a unified, context-rich audit trail of all authentication and authorization events, automatically mapped to user identity and risk score. This eliminates manual log aggregation for SOX, GDPR, or HIPAA audits, turning compliance reporting from a quarterly scramble into a continuous, automated process, saving hundreds of analyst hours.

By building a custom orchestration layer that sits atop existing IAM investments (Okta, Azure AD), you avoid vendor lock-in and costly per-feature licensing for 'adaptive' modules. The workflow unifies signals from disparate endpoint, network, and HR systems, creating a single source of truth for access policy that reduces administrative overhead and long-term total cost of ownership.

A custom-built risk engine allows you to incorporate proprietary business logic, unique data sources, and rapidly evolving threat intelligence. This creates a competitive moat in security and user experience, enabling faster adoption of new standards (e.g., passkeys) and seamless integration with future agentic workflows, protecting your IAM investment against obsolescence.

The core agent evaluates real-time signals—behavioral biometrics (typing cadence, mouse dynamics), device trust score from your MDM/EDR, geolocation, and network reputation—to generate a continuous risk score. This score determines if the SSO session can be silently extended or if step-up authentication is required, replacing static, time-based MFA rules.

Custom agents act as a policy decision point, interfacing with your identity provider (Okta, Azure AD, Ping) via SCIM and REST APIs. They inject the real-time risk score into the authentication context, dynamically influencing the acr (Authentication Context Class Reference) value to trigger conditional access policies without modifying core SSO logic.

A lightweight JavaScript agent or endpoint SDK collects anonymized user interaction data, which is streamed to a model-serving layer. Anomaly detection models compare live patterns against a learned baseline. Significant deviations automatically increase the session risk score, triggering re-authentication before credential-based attacks can succeed.

A central orchestrator (built with LangGraph or Temporal) manages the workflow: ingesting signals, calling the risk engine, enforcing decisions via the SSO layer, and logging every event—risk score, data sources, and final action—to your SIEM (Splunk, Sentinel) for compliance (SOX, GDPR) and forensic analysis.

For high-risk scores or system failures, the workflow automatically routes cases to a human-in-the-loop dashboard in your SOAR or ServiceNow instance. Security analysts receive context-rich alerts with the user's risk profile and recommended actions (block, investigate, approve), preventing authentication lockout for legitimate users during edge cases.

Deployment is phased using a feature flag and risk-score learning system. Initial rollout monitors risk scores without enforcement to calibrate models. Gradual enforcement begins with low-sensitivity applications, allowing tuning of risk thresholds and integration with existing helpdesk workflows to manage user communication and support tickets.

Owns the integration of the AI risk engine with the core SSO provider (Okta, Azure AD) and conditional access policies. Responsible for the API orchestration layer that feeds real-time risk scores into authentication decisions and session management. Ensures the workflow meets scalability, latency, and reliability requirements for enterprise-wide login traffic.

Defines the risk-scoring logic and behavioral baselines. Provides the threat intelligence feeds (e.g., IP reputation, credential leak data) that inform the contextual risk model. Establishes thresholds for step-up authentication and session revocation. Owns the monitoring and alerting for anomalous risk engine outputs and potential evasion attempts.

Champions the user experience and productivity gains of 'invisible security.' Defines the acceptable friction trade-offs and designs the user-facing flows for rare step-up authentication challenges. Measures success through user satisfaction (CSAT), login success rates, and reduction in helpdesk tickets related to MFA fatigue.

Builds and maintains the real-time data pipeline ingesting behavioral signals (telemetry from endpoint agents), device posture, and login context. Operationalizes the risk-scoring models, ensuring low-latency inference, model monitoring for drift, and seamless retraining cycles. Manages the data lake or feature store that feeds the risk engine.

Adapts support playbooks for the new paradigm, focusing on exception handling and user education. Manages the governance and approval workflows for policy changes to the risk engine. Responsible for the audit trail generation and reporting to demonstrate compliance with internal policies and external regulations (e.g., SOX, GDPR).

Provides the architectural blueprint and delivery muscle for the custom build. Handles the complex integration across SSO, endpoint detection and response (EDR), mobile device management (MDM), and SIEM systems. Delivers the orchestration logic (often using frameworks like LangGraph) and ensures the production deployment follows security-by-design and zero-trust principles.