By: Satyajeet Darji, Associate Director – Cyber & Information Security

The Evolution of Third-Party Risk: When Trust Meets Technology

Not long ago, third-party risk management seemed straightforward. A vendor questionnaire, a few compliance checks, maybe an NDA, and the job was done. Back then, the main concern was whether a vendor followed ISO standards or had updated antivirus software.

But the world changed. Attackers grew smarter, and the weakest link wasn’t inside your own systems, it was hidden deep within the supply chain. Many organizations realized that protecting internal systems alone was no longer enough. Third-party connections posed a growing and often invisible risk.

I’ve come across cases where this invisible risk became very real. A third-party payroll provider once ignored a known vulnerability in its web application—a small oversight on their side, but a major exposure for the client. Attackers used that gap to enter the system and access sensitive payroll data. The organization ended up facing breach and regulatory scrutiny, despite having strong internal controls. It was a clear reminder: the real danger often sits outside your walls, buried in the systems you trust.

From Simplicity to Shock: The Wake-Up Call

The SolarWinds attack was the turning point. It revealed that organizations were not just safeguarding themselves, they were responsible for an entire ecosystem of partners, vendors, and subcontractors. Risk management shifted from a checklist exercise to a survival strategy.

The question evolved from “Did we assess our vendor?” to “Do we truly understand who’s connected to us?” This shift opened the door for technology, especially Artificial Intelligence (AI) to redefine how we think about risk.

Beyond Third Parties: The Hidden Fourth

As organizations rethought their vendor ecosystems, another realization emerged: risk does not stop at direct vendors. It extends to their vendors, the fourth parties.

A cloud provider’s subcontractor, a data processor’s offshore analytics team, or a marketing agency’s automation vendor may never interact with you directly, yet they still handle your data. If they falter, you falter.

This invisible layer of risk is where AI truly shines. Graph analytics and machine learning models can map these hidden dependencies, uncover concentration risks, and reveal vulnerabilities that traditional assessments would miss. By exposing the unseen, AI gives organizations the power to act before the chain breaks.

During one such review of fourth-party relationships, we uncovered a case where an offshore developer working for a software supplier had far more access than necessary. It was a small detail buried deep in the chain, but it carried significant risk. That discovery pushed us to strengthen access controls across the broader vendor ecosystem. It also reinforced a larger truth: as these hidden dependencies expand, traditional oversight can no longer keep up on its own, setting the stage for AI to take a central role in managing modern third-party risk.

AI: The Hero of Modern Third-Party Risk Management

AI is no longer just a tool, it has become the backbone of third-party risk management (TRPM). It transforms risk oversight from reactive defense into proactive resilience:

1. Continuous Monitoring: AI enables real-time surveillance of vendor ecosystems, detecting anomalies instantly.
2. Predictive Risk Scoring: By correlating cybersecurity posture, financial health, and external data threat, AI forecasts vulnerabilities before they even surface.
3. Automated Document Intelligence: AI-powered Natural Language Processing (NLP) scans thousands of contracts, SOC reports, and certifications, flagging gaps humans might miss.
4. Faster Incident Response: AI-driven platforms trigger alerts and remediation workflows within minutes, not days.

Real-World Impact

• Manufacturing: A supply chain giant mapped fourth-party dependencies with AI graph analytics, uncovering hidden risks in its logistics network.
• Finance: A global bank used AI-driven monitoring to detect a vulnerability in its outsourced platform days before attackers exploited it elsewhere.
• Healthcare: Hospitals leveraged NLP to scan vendor contracts for compliance gaps, ensuring patient data remained secure.

These examples aren’t isolated wins, they show how AI provides an intelligence layer that makes TPRM dynamic, predictive, and trustworthy.

Just a few months ago, AI-powered contract analysis flagged a missing data protection clause in a new vendor agreement. The issue was corrected before onboarding began, reinforcing how intelligent automation can prevent silent risks and strengthen trust long before a partnership goes live.

From Adaptation to Advantage: Building Trust in a Connected Ecosystem

Across industries, organizations are rewriting their playbooks with AI at the center. Annual audits are giving way to continuous monitoring, digital supply chains are mapped with AI-powered graph analytics, and due diligence is increasingly automated. Predictive analytics now forecast vendor instability before it surfaces, shifting risk management from reactive defense to proactive resilience. Many organizations are embracing this approach to strengthen oversight and trust.

But adaptation alone isn’t enough. To truly gain advantage, enterprises often look for ways to embed these AI-driven practices into a cohesive, end-to-end framework. Some, like LTIMindtree, are developing AI-enabled approaches that enhance visibility, resilience, and trust across the vendor ecosystem. These approaches enable enterprises to:

• Implement continuous third- and fourth-party monitoring with predictive analytics
• Gain unified visibility through dynamic dashboards
• Automate due diligence processes using AI
• Align with global standards such as ISO 27036, NIST, and SEBI CSCRF

By integrating intelligence and automation across the TPRM lifecycle, we transform adaptation into strategic advantage. The result is not just resilience; it’s digital trust at scale, where every vendor relationship strengthens, rather than weakens, security posture.

Trust in Action: Challenges and Opportunities

The journey isn’t without challenges. Vendors may hesitate to share internal details, budgets may be tight, and assessments can feel repetitive.

But accountability cannot be outsourced. The leaders in this space treat vendor relationships as partnerships. They collaborate on fixes, share threat intelligence, and focus on mutual resilience.

This is where trust becomes the ultimate outcome. AI may be the hero, but trust is the currency it enables. Organizations embracing this mindset are not just protecting themselves, they are building credibility, resilience, and reputation.

The evolution of third-party risk management is no longer about merely avoiding breaches. It is about redefining trust in the digital age, with AI and risk management automation serving as the backbone of that trust.

References:

1. SolarWinds Attack: Play by Play and Lessons Learned, Aqua, January 18, 2023: https://www.aquasec.com/cloud-native-academy/supply-chain-security/solarwinds-attack/
2. Digital Integrated Risk Management Service Powered by ServiceNow, LTIMindtree: https://www.ltimindtree.com/services/cyber-security/governance-risk-and-compliance-service/digital-integrated-risk-management-service-powered-by-servicenow/
3. Microsoft – AI Security & Responsible AI Guidelines: https://learn.microsoft.com/en-us/security/engineering/
4. NIST – Supply Chain Risk Management: https://csrc.nist.gov/publications/detail/sp/800-161/rev-1/final