Valence SaaS and AI Security Platform

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

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

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.

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.

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

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

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.

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.

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

Monitors AI-agent behavior at runtime to detect anomalous or malicious actions and policy violations.

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.