Managing Third-Party Risk When You Have 10,000 Suppliers - with Dean Alms of Aravo

2026-02-06 · Source: The AI in Business Podcast · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cybersecurity & Data Privacy · Depth: Intermediate, long

Summary

Dean Alms, Chief Product Officer at Aravo, discusses how third-party risk management (TPRM) has evolved from a back-office compliance function into a critical board-level challenge for enterprises managing thousands of vendors, suppliers, and partners. The discussion highlights the increasing complexity due to growing regulatory mandates across industries and geographies, and the significant financial and reputational costs of non-compliance. Alms explains that traditional, slow survey-based methods often fail to provide decision-grade insights, leading to inefficiencies. He details how AI-driven automation is transforming TPRM by automating document ingestion, validating survey responses, generating corrective actions, and enabling natural language queries for risk data. These advancements reduce operational costs, improve data integrity, and facilitate a shift from episodic reviews to continuous monitoring, ultimately enhancing an organization's resilience against disruptions like cyberattacks, supply chain issues, and environmental disasters.

Key takeaway

For CTOs and VPs of Engineering/Data grappling with extensive supplier ecosystems, integrating AI into your third-party risk management strategy is crucial. You should prioritize AI solutions that automate document processing and corrective action generation to reduce operational costs and enhance data integrity. Shifting towards continuous monitoring models, supported by AI, will enable your teams to move beyond reactive risk identification to proactive remediation and long-term resilience planning, safeguarding revenue and reputation.

Key insights

AI-driven automation transforms third-party risk management from a compliance task to a strategic resilience function.

Principles

• Enterprises are accountable for supplier "sins."
• Continuous monitoring improves risk posture.
• AI is use-case specific, not "pixie dust."

Method

AI automates document ingestion, validates survey responses, generates corrective actions, and enables natural language querying for third-party risk data.

In practice

• Automate SOC 2 document analysis.
• Generate corrective actions with AI.
• Use natural language for risk data queries.

Topics

• Third-Party Risk Management
• AI Automation
• Continuous Monitoring
• Natural Language Processing

Best for: CTO, VP of Engineering/Data, Executive, Director of AI/ML, Operations Professional

Related on AIssential

• Heimdal Survey: Executives Four Times More Confident About AI Risk Than The Teams Managing It — Executives significantly overestimate AI risk control compared to practitioners, as AI adoption outpaces security measures.
• What Breaks First in Enterprise AI Systems — Prioritizing AI velocity over robust governance and architecture creates significant security and operational risks for enterprises.
• AI Is Changing Cyber Risk. Here’s How SMBs Can Respond. — AI amplifies cyber threats, but SMBs can implement affordable, foundational defenses to significantly reduce their vulnerability.
• Seven steps to AI supply chain visibility — before a breach forces the issue — AI supply chain visibility is critical for security, but current enterprise practices and traditional SBOMs are inadequate for dynamic AI models.
• Managing Third-Party Risk at Scale Without Drowning in Surveys - with Carey Smith — Continuous, AI-enabled monitoring is essential for managing multi-tier supply chain risks at scale.
• Moving from Theory to Action in AI Risk Management — Proactive corporate-level AI risk assessment is crucial for managing liabilities, fostering trust, and ensuring regulatory compliance.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by The AI in Business Podcast.