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, 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.

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.

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.

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.

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.

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.

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.

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.