Reco

Maps integration connections between SaaS applications (API keys, webhooks, shared credentials) to surface unmanaged data flows and integration attack surface.

Automatically corrects specific SaaS misconfigurations or revokes excessive permissions without manual intervention.

Maps SaaS configuration findings to CIS SaaS Benchmarks, NIST 800-53, and SOC 2 control requirements, generating evidence for auditors from automated assessment.

Discovers OAuth-connected third-party applications with access to core SaaS environments, maps their granted permissions, and flags high-risk or unused authorizations for revocation.

Integrates with enterprise SaaS applications through native admin APIs to assess tenant configuration, identity, and third-party connections, including Microsoft 365, Google Workspace, Salesforce, Slack, GitHub, ServiceNow, and Okta. Breadth and depth of coverage vary by product.

Identifies over-privileged users, dormant accounts, and excessive license assignments within SaaS applications, producing a right-sizing recommendation per application.

Detects sensitive content shared publicly or externally from connected SaaS apps, such as world-readable files, anonymous share links, and over-shared folders, so exposure can be revoked before it leaks.

Maps data lineage and provenance across AI training and inference pipelines, tracing how PII, PHI, and IP move into models and external services.

Assesses the identities and service accounts that AI models, pipelines, and agents use, flagging over-permissioned non-human identities and access paths that violate least privilege. Reports identity risk as a posture finding, distinct from enforcing access policies at the model API at runtime.

Scores deployed AI models by risk level based on data sensitivity processed, deployment scope, capability classification, and applicable regulatory requirements.

Automatically discovers AI models, LLM API connections, ML pipelines, and AI-enabled SaaS applications in use across the organization, including those deployed without IT authorization.

Detects sensitive or regulated data in AI training, fine-tuning, or third-party LLM flows without appropriate controls, such as unencrypted PII in inputs or PHI sent to external APIs.

Discovers AI model and inference endpoints and flags public exposure, weak authentication, default credentials, or excessive permissions as posture misconfigurations.

Discovers and enforces least-privilege access for non-human and AI-agent identities across systems and data.