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.

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

Discovers the third-party applications, marketplace plug-ins and add-ons, and AI agents connected to core SaaS environments through OAuth grants, API tokens or MCP, maps the permissions each has been granted, and flags high-risk, over-scoped or unused connections for revocation, so that user-installed integrations do not become an unmanaged path to tenant data.

Maps SaaS configuration findings to CIS SaaS Benchmarks, CISA SCuBA secure configuration baselines, NIST 800-53, ISO 27001 and SOC 2 control requirements, and generates auditor-ready evidence from automated checks rather than manual screenshots, so that SaaS posture can be validated against published standards as they emerge.

Compares each connected SaaS tenant's security settings against vendor best practices and the customer's own baselines, reports misconfigurations and drift, and explains the fix, with the depth of checks and advice varying by application. Coverage of enterprise SaaS applications through native admin APIs, including smaller business-critical apps, also varies by vendor.

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

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

Inventories the generative AI features embedded in SaaS applications and the AI agents and assistants granted access to SaaS tenants, including connections made through MCP, showing which models are in use, how they connect and what data and permissions they hold, so that shadow AI inside sanctioned SaaS is governed alongside other integrations.

Analyzes SaaS application activity logs to detect compromised credentials, stolen session tokens, insider misuse and anomalous behavior by human and non-human identities inside and across SaaS applications, so that the most common SaaS breach path is caught within the SaaS estate and not only at the identity provider.

Discovers SaaS applications in use that are not yet connected to the platform, using identity provider logs, browser telemetry, email and API signals rather than network traffic, so that unsanctioned and unmanaged tenants can be brought under posture management. Discovery depth varies widely across vendors.

Discovers the AI models, agents, MCP servers and AI-enabled applications in use across the organization, including those deployed without IT authorization, and keeps them in an inventory that records type, ownership and criticality. Discovery runs through traffic inspection and code repository scanning at minimum; some products add cloud provider, application server or AI engineering tool integrations. The inventory anchors exposure management and risk scoring.

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.

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

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.

Monitors what AI agents do at runtime, including tool calls, data access and multi-step actions, and flags anomalous or malicious behavior, policy violations and drift from the user's stated intent as prioritized findings that can be forwarded to the SOC or a SIEM. The row covers detection and alerting on agent behavior; inline blocking and containment of agent actions sit with LLM Security.

Maps AI posture findings and the controls in place to AI security and regulatory frameworks such as the EU AI Act, NIST AI RMF and ISO/IEC 42001, and reports coverage and open gaps per framework for audits, regulators and leadership. The reporting draws on the AI inventory and its findings; approval workflows, attestations and evidence management belong to AI governance platforms.