TPRM Tools in 2026: Ranked by Privacy & AI Data Use

The Blind Spot in Vendor Risk Management

Third party risk management tools are designed to protect enterprises from the risks introduced by external vendors. They evaluate cybersecurity posture, compliance gaps and operational vulnerabilities across the supply chain. But there is a critical blind spot in this model: nobody is evaluating the AI privacy practices of the TPRM tools themselves. In 2026, nearly every major TPRM platform has embedded artificial intelligence into its core operations. AI now powers questionnaire automation, evidence analysis, risk scoring and continuous monitoring. This means your most sensitive vendor data, compliance documentation and internal risk profiles are being processed by AI models inside these platforms. The question enterprises must ask is simple: what happens to that data?

OneTrust

OneTrust is widely regarded as a market leader. It earned a Forrester Wave Leader designation in 2025 for privacy management and was recognized in the IDC MarketScape for data privacy compliance. The platform offers dedicated AI governance modules aligned with the EU AI Act, NIST AI Risk Management Framework and ISO 42001. It provides model inventories, risk assessments, policy enforcement and automated compliance workflows. OneTrust serves over 14,000 customers globally, including more than half of the Fortune 500. However, when it comes to its own AI data practices, the transparency gap persists. OneTrust processes massive volumes of vendor risk data, compliance evidence and privacy assessments through its own AI features. Enterprises that rely on OneTrust to govern AI in their vendor ecosystem rarely turn the same lens on OneTrust itself. How is vendor data used to train or improve its AI models? What retention policies apply to data processed by its AI governance engine? These questions remain largely unanswered in publicly available documentation, creating a blind spot for enterprises subject to GDPR Article 22 and CCPA data rights requirements.

Drata

Drata has expanded aggressively since its founding, now supporting over 20 compliance frameworks including SOC 2, ISO 27001, HIPAA, GDPR, and notably ISO 42001 for AI risk management. The platform serves more than 7,000 customers worldwide, with notable clients including OpenAI, Notion and PagerDuty. Drata uses AI to automate evidence collection, analyze SOC 2 reports and accelerate vendor assessments. Its recent acquisitions of SafeBase, oak9 and Harmonize have expanded its capabilities into trust centers, developer security and AI powered anomaly detection. The compliance automation market is projected to grow from $2.94 billion in 2024 to $13.4 billion by 2034, and Drata is positioned to capture a significant share. Yet the same AI that makes Drata efficient also ingests sensitive compliance documentation at scale. For enterprises in regulated industries, the absence of independent verification of how Drata's AI handles, retains or potentially reuses this data represents an unaddressed risk under both GDPR and CCPA.

Vanta

Vanta has built its reputation on speed and simplicity. The platform supports over 35 compliance frameworks and integrates with more than 375 tools across cloud providers, HR systems, developer platforms and identity management solutions. Vanta's AI agent runs over 1,200 automated compliance checks per hour, scanning environments for control failures, misconfigurations and policy violations. The company reached $100 million in annual recurring revenue in 2024 and was recognized in the IDC MarketScape for GRC software. Vanta emphasizes data governance in its positioning, but the focus is directed outward toward its customers' vendors rather than inward toward its own AI operations. When Vanta's AI scans your AWS configurations, Okta user logs and GitHub repositories, it collects and processes data that falls squarely under GDPR and CCPA protections. Independent evaluation of how Vanta's AI handles this data, whether it is used for model improvement and what opt out mechanisms exist for data subjects, remains difficult to find.

SecurityScorecard

SecurityScorecard takes a fundamentally different approach. Rather than requiring vendor cooperation, it provides outside in risk ratings by scanning external digital footprints. The platform uses AI driven analytics to assess cybersecurity posture based on observable data such as DNS configurations, exposed vulnerabilities, patching cadence and network behavior. This methodology is powerful for rapid vendor assessment, but it also raises specific privacy questions. SecurityScorecard collects data about organizations without their direct participation or explicit consent. Under CCPA, which grants consumers the right to know what personal information is collected and how it is used, the passive data collection model creates potential compliance friction. Enterprises relying on SecurityScorecard should consider whether the platform's data collection practices align with their own privacy obligations.

The Industry Pattern

Beyond these four, platforms like Prevalent, Riskonnect, UpGuard, BitSight and ProcessUnity all incorporate AI capabilities that process vendor data at scale. The pattern is consistent across the industry: TPRM tools evaluate everyone else's risk but face minimal scrutiny regarding their own AI data governance. According to Gartner, 40 percent of organizations have already experienced an AI privacy breach. The tools designed to prevent vendor risk incidents may themselves represent an unmanaged source of risk. This is not a theoretical concern. As regulators sharpen their focus on AI governance under frameworks like the EU AI Act, enterprises will face increasing pressure to demonstrate that every AI powered tool in their stack, including their TPRM platform, meets privacy and governance standards.

The Missing Layer: Independent AI Governance Assessment

TrustThis.org was created to address precisely this gap. The platform applies the AITS (AI Trust Score) methodology to independently evaluate how software applications handle AI data privacy and governance. The assessment covers 20 criteria aligned with GDPR and CCPA requirements, examining areas such as AI training data disclosure, automated decision making transparency, data retention policies, opt out mechanisms and third party data sharing practices. Unlike traditional security ratings that focus on cybersecurity posture, the AITS methodology specifically targets the intersection of AI functionality and privacy compliance. For CISOs, compliance teams and procurement leaders, the message is clear. Your TPRM platform is a critical piece of your governance infrastructure. It handles your most sensitive vendor data and uses AI to process it. Before you trust it with that responsibility, verify that the platform itself earns your trust. Complement your TPRM evaluations with independent AI governance assessments. Visit TrustThis.org to see how leading platforms score when the audit lens is turned inward.