Third-party relationships form the backbone of most organizations, but they also create layers of exposure. Vendors, suppliers, and partners hold access to sensitive data and critical processes. Any weak link can introduce financial, reputational, or compliance risks. Traditional risk management approaches often struggle with the scale and complexity of monitoring these external parties.

AI-powered risk intelligence transforms this challenge. By leveraging algorithms that learn from vast data, AI systems can analyze, anticipate, and signal potential risks with unprecedented accuracy. They extract insights from contracts, regulatory filings, cyber alerts, and even unstructured online chatter, reducing the need for manual oversight.

» Ensure your cybersecurity is up to standard with KELA

The Urgency for AI Adoption Stems From Three Realities:

• Supply chain complexity – Global vendor networks multiply exposure points.

• Regulatory acceleration – Frameworks like DORA, NIS2, and HIPAA demand real-time oversight.

• Rising cyber threats – Attackers increasingly target vendors as entry points.

A notable frontier in this space is agentic AI—a form of goal-oriented AI capable of not just analyzing risks, but also initiating responses. While this article explores the foundations of AI in third-party risk management (TPRM), agentic AI will be discussed later as a transformative evolution.

» Make sure you understand the most targeted entry points by attackers

AI in TPRM

Take control of third-party risks—see how KELA’s AI identifies threats before they escalate.

Learn More



Core AI Capabilities in TPRM

Artificial Intelligence brings precision and scalability to third-party risk management by combining multiple capabilities that work together across the vendor lifecycle. Each capability addresses a specific challenge—whether it’s processing large datasets, identifying early warning signals, or delivering actionable insights.

Data Aggregation & Enrichment

AI systems pull information from a wide range of structured and unstructured sources to build a complete vendor risk profile. Examples include:

• Public registries and regulatory filings

• Cybersecurity advisories and incident databases

• Social media sentiment and news reports

• Internal records such, as contracts and audits

This aggregation eliminates silos and provides a 360-degree view of vendor risk exposure.

» Here's everything you need to know about TPRM

NLP & Document Intelligence

Natural Language Processing (NLP) allows systems to “read” documents at scale, saving risk teams from manual review. Key functions include:

• Parsing contracts to identify liability clauses or missing controls

• Reconciling questionnaire responses with evidence

• Highlighting potential red flags in supplier policies

With document intelligence, organizations reduce time-to-insight and improve consistency in compliance checks.

Predictive Analytics & Scoring Models

Machine learning models find patterns invisible to human analysts. These models can:

• Forecast the likelihood of vendor disruptions or non-compliance
• Dynamically adjust risk scores as new data arrives
• Benchmark vendors against industry baselines

Continuous Monitoring & Alerts

Instead of annual or quarterly assessments, AI enables real-time surveillance of third-party risk signals. These can include:

• Regulatory penalties or litigation filings

• Cyber incidents tied to vendor domains

• Negative press or reputational events

• Operational anomalies such as supply chain delays

Early alerts mean organizations can intervene proactively, reducing the impact of emerging risks.

AI-Powered Third-Party Risk Management

Stop juggling vendor risks manually. KELA’s AI platform handles it efficiently for you.

Continuous monitoring to catch threats in real-time

Gain visibility over your vendors' security posture

Focus on high-risk vendors for faster action

Contact Us



» Worried about security? Here are the reasons you need cyber threat intelligence

AI Use Cases Across the TPRM Lifecycle

AI-powered tools are not just theoretical—they reshape every stage of third-party risk management, from initial onboarding to continuous oversight. By embedding AI into the lifecycle, organizations improve both efficiency and accuracy in identifying, monitoring, and mitigating risks.

Vendor Onboarding & Due Diligence

Traditional onboarding requires lengthy manual checks across dozens of data sources. AI streamlines this process by:

• Automating data collection from regulatory databases, credit reports, and cyber intelligence feeds

• Building vendor risk profiles that consolidate financial, operational, and security indicators

• Prioritizing assessments so high-risk vendors are flagged for deeper review

This reduces onboarding time from weeks to days, while ensuring no critical risk signals are overlooked.

» Confused? Here's our guide to navigating third-party cyber threats

Contract Analysis & Compliance Checks

Contracts are dense and often inconsistent across vendors. AI supports legal and compliance teams by:

• Identifying risk-bearing clauses (e.g., data residency, liability, subcontracting)

• Flagging SLA deviations compared to internal standards

• Detecting gaps in compliance with frameworks like GDPR, HIPAA, or DORA

With NLP-driven analysis, organizations can standardize compliance checks and reduce disputes with vendors later.

» Learn more: Why Third-Party risk in healthcare demands immediate attention

Ongoing Risk Surveillance

Risk does not end after onboarding. AI enables continuous monitoring that flags:

• Anomalies in vendor behavior or system availability

• Breach indicators tied to vendor assets (domains, certificates, IP addresses)

• Shifts in reputation, such as negative press or regulatory scrutiny

Early detection prevents escalation into major incidents.

Dashboarding & Risk Reporting

Executives and boards require clarity, not raw data. AI-powered visualization tools turn complex information into:

• Interactive dashboards that map risk levels across vendor tiers
• Heat maps showing the concentration of risk in certain geographies or industries
• Predictive trend lines that illustrate potential future risk exposure

By embedding AI at each stage, organizations transition from reactive to proactive TPRM—anticipating issues before they disrupt operations.

» Did you know? Cybercriminals now exploit generative AI

Agentic AI, The Autonomous Edge

While traditional AI focuses on analysis and reporting, agentic AI introduces a new level of autonomy. Instead of waiting for human instructions, agentic systems are designed to pursue goals, initiate actions, and coordinate workflows across multiple tools.

What Is Agentic AI?

Agentic AI is a subset of artificial intelligence that operates with intent and autonomy. Unlike static models, these agents can:

• Monitor data streams continuously

• Decide when action is needed

• Trigger workflows or escalate alerts automatically

This shifts AI from being a supportive assistant to a semi-autonomous partner.

» Find out how agentic AI is transforming cybersecurity

Key Capabilities

• Autonomous Action: Agents can launch predefined tasks, such as notifying vendors of compliance gaps or initiating access reviews.

• Multi-Agent Workflows: Different agents collaborate, each handling a subtask. For example: - Agent A scans regulatory updates - Agent B cross-references affected vendors - Agent C updates dashboards and alerts compliance officers

Together, they complete complex, multi-step workflows without continuous human input.

Relevance in TPRM

Imagine an agent that:

• Tracks new cybersecurity regulations in Europe

• Identifies which vendors are impacted

• Updates their risk scores automatically

• Generates a compliance adjustment plan

• Logs the entire process for audit review

This level of orchestration enhances responsiveness while reducing manual burden.

Risks & Governance

Autonomy must come with guardrails. Without oversight, agentic AI could make inappropriate or incomplete decisions. Governance structures should include:

• Audit trails documenting every AI action
• Human-in-the-loop checkpoints for high-impact decisions
• Oversight committees ensuring AI aligns with policies

Agentic AI is not about replacing human expertise—it’s about extending capacity while ensuring accountability and control.

» Ready to begin? Contact us to learn more about our third-party intelligence

Implementation Strategy

Deploying AI in third-party risk management requires more than just technology—it demands a structured approach that balances innovation with oversight. A successful strategy ensures that AI strengthens risk programs without creating new vulnerabilities.

Define Objectives & Scope

Organizations should begin by identifying high-impact areas where AI can deliver measurable value. Examples include:

• Onboarding risk – accelerating due diligence and vendor profiling

• Contract clarity – standardizing compliance and SLA reviews

• Real-time alerts – detecting regulatory or cybersecurity incidents quickly

Starting with clear goals prevents scope creep and allows teams to measure ROI effectively.

» Make sure you understand the difference between vulnerability, threat, and risk to strengthen your cybersecurity strategy

Build Iteratively

Rather than deploying advanced autonomy immediately, risk teams benefit from an incremental approach:

1. Supervised AI – Start with NLP-powered contract reviews and predictive risk models.

2. Augmented Analytics – Add continuous monitoring and dynamic dashboards.

3. Agentic AI – Introduce semi-autonomous agents cautiously, once governance controls are established.

This phased adoption reduces disruption and builds organizational trust in AI outputs.

Enable Human Oversight

AI should act as a co-pilot, not a replacement. Oversight mechanisms include:

• Threshold tuning – Human experts refine alert sensitivity to balance false positives and missed risks.
• Review cycles – Risk teams validate AI assessments regularly.
• Feedback loops – Analysts provide corrections that improve model accuracy over time.

By combining AI with structured governance and incremental rollout, organizations gain the benefits of automation while retaining confidence and accountability in decision-making.

» Make sure you understand how threat actors breach and exploit your data

Benefits of AI-Powered Risk Intelligence

AI-powered risk intelligence transforms third-party risk management by combining speed, accuracy, and adaptability. Organizations that integrate these capabilities experience measurable improvements across performance, oversight, and resilience.

Speed & Scale

AI can process thousands of vendor records, contracts, and data feeds in a fraction of the time it would take humans.

• Vendor onboarding timelines shrink from weeks to days.

• Continuous monitoring ensures instant alerts, not quarterly surprises.

• Agentic AI further accelerates processes by initiating follow-up actions automatically.

Insight & Accuracy

By drawing on diverse structured and unstructured data sources, AI reduces blind spots that manual reviews often miss.

• Predictive insights help forecast which vendors may experience regulatory or financial trouble.

• Cross-referencing signals increases reliability, limiting dependence on a single data type.

• Advanced scoring models adapt as new risks emerge.

Efficiency Gains

Routine tasks like questionnaire reconciliation or SLA checks consume valuable team resources. AI automates these processes so governance, risk, and compliance (GRC) professionals can focus on strategic priorities.

Resilience & Robustness

AI-driven systems evolve with the risk environment. Unlike static frameworks, they adapt as:

• Regulatory requirements shift (e.g., DORA, NIS2, HIPAA).

• New threat vectors emerge in cybercrime or supply chains.

• Business priorities evolve, requiring tailored risk thresholds.

By continuously learning and adjusting, AI strengthens the organization’s long-term risk posture.

When executed well, AI-powered risk intelligence does not just cut costs—it enhances trust, credibility, and resilience across the vendor ecosystem.

» Stay in the loop for our upcoming GRC agents — Be among the first to engage with KELA's evolving autonomous agent ecosystem as it rolls out.

Future Outlook

AI in third-party risk management is not static, and it’s evolving rapidly. The trajectory points toward systems that are more autonomous, interconnected, and embedded into daily risk operations.

From Insight to Autonomy

Current AI excels at analysis and prediction, but the next phase is about taking action safely. Semi-autonomous agents will:

• Trigger alerts and assign tasks automatically

• Initiate remediation steps, such as requesting updated vendor compliance documents

• Escalate issues directly to risk owners with minimal human delay

This evolution shifts TPRM from reactive oversight to proactive orchestration.

Ecosystem Collaboration

AI will increasingly connect across risk domains, breaking down silos that limit visibility. Integration will span:

• Compliance – mapping regulatory updates directly to affected vendors

• Cybersecurity – linking threat intelligence with supply chain data

• ESG metrics – aligning vendor performance with sustainability commitments

This interconnected ecosystem provides leaders with one source of truth for enterprise-wide risk.

» Learn how supply chain threat intelligence strengthens your security posture

Organizational Maturity

Future TPRM teams will be hybrid by design, combining:

• AI-savvy analysts who interpret and challenge AI findings

• Oversight specialists who ensure regulatory alignment and governance

• Agentic AI systems that manage continuous monitoring and workflow automation

How AI Strengthens Third-Party Oversight

Integrating AI into your third-party risk management enables proactive detection, continuous monitoring, and predictive insights. By combining human expertise with semi-autonomous systems, organizations reduce risk exposure, accelerate decision-making, and build a resilient vendor ecosystem.

The result is an adaptive model where humans and machines operate in tandem, ensuring oversight while scaling capabilities. The shift is already underway. Forward-looking organizations are experimenting with agentic AI to amplify their TPRM programs.

» Try Agentic AI with Kela’s CTI analysis — Experience real-time risk detection powered by next-gen AI agents