Drata Agentic Trust Management Platform

Uses AI agents to carry out GRC tasks with limited human direction, such as mapping requirements to controls, reviewing collected evidence, recommending control applicability, and triaging risks, going beyond fixed rule-based automation. Agentic maturity varies widely across products.

Manages security policies and collects employee attestations to support compliance.

Maps controls across multiple frameworks and crosswalks overlapping requirements to reduce duplicate work.

Provides a natural-language interface to query the GRC program and generate workflows, narratives, and reports, letting practitioners ask questions and draft content without building queries or templates by hand.

Automatically and continuously collects control evidence from connected systems for audit readiness.

Continuously tests and monitors control operation and flags failures across the environment.

Provides prebuilt control libraries mapped to frameworks such as SOC 2, ISO 27001, NIST CSF, PCI DSS and HIPAA.

Supports configuration of assessment questionnaires, evidence collection workflows, approval routing, and report templates without professional services or platform code changes.

Publishes customer-facing trust centers and compliance status reports.

Prepares audit-ready evidence packages and lets external auditors and certification bodies run the audit inside the platform through role-based access, managing information requests, evidence review and findings in one place, with partner audit firms able to deliver the engagement end to end.

Provides connectors to cloud, identity, HRIS, MDM and ticketing systems to automate evidence collection.

Collects control evidence from CI/CD pipelines and code repositories, such as peer review on merged changes, pipeline security checks and deployment approvals, and can fail a pipeline stage when a change breaks a compliance policy, so that frequently releasing DevOps teams stay continuously audit-ready without manual screenshots.

Generates environment-specific remediation steps for failing controls and tests, such as infrastructure-as-code or command-line fixes for a cloud misconfiguration, and answers follow-up questions in context, so that engineers can close findings without translating a control requirement into a technical fix themselves.

Turns identified risks into tracked findings and issues with owners, due dates and action plans, routes them through escalation and exception or risk-acceptance approval, recommends or preconfigures the remediation workflow, and reports status until closure.

Profiles each third party at intake, capturing criticality, data sensitivity, service type, geography and regulatory requirements, to determine which risk domains apply to it and to scope the depth and cadence of assessment accordingly.

Distributes, collects and scores third-party assessments and security questionnaires from a maintained template library that spans risk domains and standards, with evidence requests, reminders, reviewer collaboration and scoring rules; stronger implementations scope questionnaire depth and cadence dynamically from the third party's risk profile rather than sending one template to every vendor.

Scores each third party's inherent and residual risk and measures its potential impact on the business or supply chain to produce an impact estimate, aggregating domain-level results into a composite score that can be rolled up across the portfolio and correlated with enterprise objectives and control performance.

Assigns each third party to a risk tier from its inherent risk profile and business criticality, with tier definitions and thresholds the customer can change, and uses the tier to set assessment depth, review cadence, approval routing and monitoring intensity so that workflows adjust automatically when a third party's tier changes.

Reads third-party-supplied documents such as SOC 2 reports, ISO certificates, policies and prior questionnaires with AI, extracts the relevant answers and evidence to prepopulate assessment responses, and evaluates submitted responses for gaps or inconsistencies so reviewers work the exceptions rather than reading every document.

Applies AI across the third-party data set to classify and score risk, flag red flags and emerging risk, recommend responses, and generate summaries and risk reports on demand, including natural-language questions over the third-party repository, rather than relying on analysts to read every record.