Written by: Bob Maley, Chief Security Officer. Originally published on LinkedIn.

Let’s be honest, Third-Party Risk Management (TPRM) is tough. If you’re in the trenches dealing with vendor assessments, continuous monitoring, and the ever-growing compliance landscape, you know the feeling. The sheer scale is daunting, hundreds, maybe thousands of vendors, each representing a potential risk vector. Keeping up feels like trying to drink from a firehose, especially when business units are clamoring for faster onboarding and resources are always in short supply. We’re drowning in questionnaires, struggling to connect disparate data points, and constantly worried about the “unknown unknowns” lurking in our supply chain.

Into this challenging environment steps the seemingly perfect savior: Artificial Intelligence.

Scroll through your feed or attend any industry conference, and you’ll hear the siren song. Vendors are falling over themselves to tell us how their “AI-powered platform” will revolutionize TPRM. They promise end-to-end automation, predictive insights that stop breaches before they happen, effortless compliance, and the ability to finally get ahead of the curve. It’s an incredibly seductive narrative, especially when you’re feeling overwhelmed by the daily grind of managing third-party risk. They paint a picture where complex assessments happen automatically, risks are flagged with uncanny accuracy, and your team is freed up for purely strategic work. Who wouldn’t want that?

And here’s the thing: AI can be genuinely helpful in TPRM. I’ve seen it myself, and dismissing it entirely would be foolish. There are specific, practical applications where it offers real value.

  • Think about the drudgery of processing vendor documentation. AI, particularly natural language processing, can help parse things like SOC 2 reports or security questionnaires, extracting key information much faster than a human could.
  • It can help scrape public data sources for negative news or breach notifications as part of continuous monitoring.
  • We’re seeing AI used to help standardize initial vendor tiering based on inherent risk factors or to identify anomalies in vendor behavior patterns that require closer analysis by an analyst.

These applications focus on automating data-intensive and repetitive tasks, while augmenting the capabilities of our human teams. They can bring real efficiency gains and help analysts focus their attention where it’s needed most. Used correctly, AI is a powerful tool for specific jobs within the broader TPRM function.

But, and this is a crucial “but”, the narrative that AI, on its own, will fix TPRM is where I believe things go off the rails.

It’s the difference between selling a powerful drill and claiming that the drill will build the entire house by itself. The reality is far more nuanced, and the limitations of AI in the complex world of risk management are significant, often overlooked in the marketing hype.

First and foremost, AI is fundamentally dependent on data.

The old adage “garbage in, garbage out” has never been more relevant. For AI algorithms to provide meaningful insights in TPRM, they need vast amounts of clean, accurate, relevant, and well-structured data. Anyone working in TPRM knows that getting such data across a diverse vendor portfolio is a monumental challenge in itself. Vendor responses can be incomplete or inconsistent, data formats vary widely, and integrating information from internal systems, external feeds, and vendor attestations is often a messy and ongoing struggle.

An AI model trained on poor or incomplete data won’t magically produce accurate risk assessments; it will likely automate bad decisions or generate unreliable outputs, potentially creating a dangerous false sense of security.

Then there’s the challenge of context and nuance.

TPRM isn’t just about crunching numbers or identifying keywords. It involves understanding the specific relationship with a vendor, the criticality of the service they provide, the potential business impact of a disruption, and interpreting qualitative information that doesn’t fit neatly into data fields.

Can an AI truly understand the subtle implications of a qualified opinion in an audit report, or grasp the cultural factors influencing a vendor’s security posture? Can it weigh the strategic importance of a partnership against a specific control deficiency? These are areas where human judgment, experience, and critical thinking remain indispensable. AI struggles to replicate this deep contextual understanding, which is often the deciding factor in complex risk decisions.

Explainability, or the lack thereof, is another major hurdle.

Many AI models, particularly complex machine learning algorithms, operate as “black boxes.” They can produce an output,  like a risk score, but tracing back why they arrived at that specific conclusion can be incredibly difficult, if not impossible.

In a field like TPRM, where decisions must be justifiable to auditors, regulators, and internal stakeholders, this lack of transparency is a significant issue. If you can’t explain how a vendor was assessed or why a particular risk was flagged (or missed), how can you truly trust the system or defend your program?

We also can’t ignore the potential for algorithmic bias.

AI models learn from the data they are trained on. If that historical data reflects existing biases – perhaps in how certain types of vendors were assessed in the past – the AI can perpetuate and even amplify those biases at scale. This could lead to unfair treatment of certain vendors or blind spots in risk assessment, undermining the very fairness and objectivity the AI was supposed to enhance. Addressing bias requires careful data curation, ongoing model monitoring, and a commitment to fairness that goes beyond simply deploying the technology.

Furthermore, the idea that implementing AI is a simple, plug-and-play affair is misleading.

Integrating AI tools effectively into existing TPRM workflows requires significant investment, specialized expertise, and careful change management. This includes ensuring data feeds are robust, training staff on how to use and interpret the outputs, and managing ongoing maintenance and potential model retraining. It’s not a switch you just flip on.

This brings me back to my core point: the danger lies in vendors leading with AI as the solution.

When the sales pitch focuses solely on the magic of AI, it distracts from the foundational elements that must be in place for any TPRM program, AI-assisted or not, to be effective. You can have the most sophisticated AI engine in the world, but if your underlying TPRM governance is weak, your processes are poorly defined, your data is a mess, and your team lacks the necessary skills for oversight and interpretation, the AI won’t save you. It might just help you drive off a cliff faster.

Focusing exclusively on the shiny new AI tool risks creating a dangerous over-reliance,  an “automation bias” so to speak,  where teams implicitly trust the AI’s output without sufficient critical validation. It can lead to neglecting the hard work of improving data quality, maturing assessment methodologies, and developing the critical thinking skills of TPRM analysts.

So, what’s the answer? It’s not about rejecting AI altogether. It’s about putting it in its proper place.

Effective, resilient Third-Party Risk Management requires a holistic approach, a sturdy foundation built on several key pillars.

  • We need strong governance with clearly defined policies, roles, responsibilities, and a well-understood risk appetite.
  • We need mature, well-documented processes covering the entire vendor lifecycle, from onboarding and due diligence through continuous monitoring and offboarding.
  • We need a focus on data quality and integration, understanding where our critical vendor data resides, how to collect it reliably, and how to make it usable.
  • Crucially, we need skilled people, analysts with the expertise to interpret complex information, exercise critical judgment, manage vendor relationships, and make nuanced risk-based decisions.
  • Technology, including AI, serves as the fifth pillar, supporting and enhancing the other four. AI tools should be chosen strategically to solve specific problems within this framework – automating laborious tasks, processing large datasets for monitoring, and providing analysts with better data to inform their judgment, not as a replacement for the framework itself. The goal should be human-machine teaming, leveraging the strengths of both: the processing power and pattern recognition of AI combined with the contextual understanding, critical thinking, and strategic decision-making of humans.

When evaluating TPRM solutions, we need to cut through the marketing buzzwords.

  • Ask vendors how their AI works.
  • What data does it need?
  • How is it trained?
  • How do they address bias and ensure explainability?
  • What level of human oversight is required?
  • Demand evidence of how the tool improves actual risk outcomes, not just claims of efficiency.

AI has the potential to be a valuable asset in our TPRM toolkit. But it’s just that – one tool among many. It’s not a magic wand, and it certainly won’t fix a broken TPRM program. Let’s stop chasing the AI silver bullet and focus instead on building strong, resilient foundations.

We can use technology, including AI, thoughtfully and strategically to support our ultimate goal: effectively managing and reducing third-party risk.

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