AuthMind AI Identity Observability & Protection Platform

Analyzes identity telemetry (authentication events, access patterns, privilege use) in real time with behavioral baselines and risk scoring; leading implementations detect identity attacks in sub-second time.

Executes response actions against active identity attacks through playbooks with configurable automation: session revocation, credential reset, account isolation, inline step-up authentication or access denial at the identity provider, and follow-up policy and configuration hardening so the same attack cannot recur.

Reconstructs an identity incident end to end (authentications, token issuance, MFA events, privilege and group changes, directory and policy modifications) into an identity-centric timeline with blast-radius context, so analysts can scope a compromise and choose the right remediation quickly. Distinct from generic SIEM case management: the pivot is the identity and the IAM objects it touched.

Detects named identity attack techniques with purpose-built detection content: password spraying, credential stuffing, pass-the-hash and pass-the-ticket, Kerberoasting, DCSync and DCShadow, golden and silver tickets, and consent phishing of OAuth applications, each mapped to MITRE ATT&CK so technique coverage can be verified against known identity attack scenarios. Complements Identity Behavioral Analytics (anomaly-based) and Identity Infrastructure Attack Detection (attacks on the IAM control plane).

Detects attacks on the IAM infrastructure itself: misuse of directory and identity provider administrator credentials, changes to token-signing certificates and federation trust, tampering with conditional access, MFA, and admin role configuration, and other signs that an identity tool has been compromised, continuously monitoring root and global administrator accounts and their configuration changes.

Exchanges identity risk signals with identity providers, IGA, PAM, endpoint, and SIEM or SOAR platforms through bidirectional integrations and the Shared Signals Framework (CAEP, RISC), so a detection can revoke a session or force step-up in the identity provider within seconds and lands in the SOC as an enriched, correlated alert instead of a siloed one.

Detects credential-abuse techniques that defeat authentication controls, including MFA circumvention, session hijacking, and forged or replayed tokens.

Integrates identity data, activity, relationships, and configuration from directories, identity providers, IGA, PAM, cloud platforms, and SaaS applications, including applications not yet connected to any IAM tool, into one correlated inventory of every human and non-human actor with its accounts and entitlements, the single view on which posture assessment and analytics run.

Flags identity-object hygiene problems: dormant and orphaned accounts, accounts without MFA enrolled, shared or generic accounts, weak or non-expiring passwords, and risky discretionary permissions, so they are cleaned up before attackers use them. Configuration of the identity providers and access policies themselves is covered by IAM Policy and Configuration Assessment.

Discovers service accounts, OAuth apps, API keys, JWT tokens, and Kubernetes service accounts alongside human accounts, mapping the complete identity population.

Fixes identity posture findings instead of only reporting them: revokes unused or excessive entitlements, enforces MFA, disables dormant accounts, and corrects policy drift, either directly or through IGA, PAM, and identity provider connectors, with approval workflows for higher-risk changes. Distinct from ITDR response actions, which act on active attacks.

Continuously assesses the security configuration of identity providers, directories, and access policies themselves (conditional access rules, federation and token-signing settings, MFA enforcement scope, admin role assignments, password and session policies) against baselines and best practices, flagging drift, gaps, and inconsistencies in the policies that decide who or what can access resources, when, and under which conditions.

Models identities, permissions, and trust relationships as a directed graph and traces the chained attack paths an adversary could follow from any identity to the organization's most critical assets, instead of scoring identity misconfigurations in isolation.

Compares granted permissions against observed usage to identify entitlements that exceed what an identity actually needs, candidates for right-sizing or revocation.

Surfaces indicators of identity compromise from posture and activity telemetry (anomalous login sequences, MFA fatigue patterns, impossible travel, sudden privilege changes) and routes them for response, so posture findings and active-attack signals sit in one risk view. This is the posture-layer signal: real-time detection, response playbooks, and recovery are the Identity Threat Detection and Response (ITDR) niche vocabulary, and EDR identity detection covers endpoint-side behavior.