The Need for Speed in TPRM

AI is transforming how CISOs and risk managers handle third-party risk management, enabling faster assessments, automated insights, and more efficient workflows. But speed comes with a hidden danger: hallucinations. AI can generate outputs that appear confident and authoritative but can lack supporting evidence – creating false confidence across the organization.

However, these hallucinations aren’t minor errors. They can undermine risk decisions, trigger rework, and even introduce operational or regulatory exposure. Effective TPRM balances speed with traceability, using automation to accelerate workflows while ensuring every insight remains accurate, verifiable, and defensible.

Let’s look at hallucinations across key areas including: questionnaires, supply chain visibility, and alerts/cyber event detection and alerts filtration. 

Questionnaires

Questionnaires are a common source of hallucinations. AI-generated answers may read as complete, yet often lack traceable documentation. That “sounds right” output doesn’t save time – it forces teams to recheck documents and validate claims before they can trust the result. But as automation grows, hallucinations can spread beyond questionnaires, reinforcing the need for evidence-backed AI across all workflows.

Supply Chain Visibility

Hallucinations also appear in supply chain assessments. Accurate risk decisions depend on knowing exactly who and what is in your supply chain, yet many organizations lack a complete view of third-, fourth-, and Nth-party relationships. Automated supply chain discovery maps vendor relationships and highlights critical suppliers. This visibility is especially important as AI technologies become embedded across vendors, sometimes with elevated or privileged access. Without accurate mapping, AI can misattribute risks, generate false positives, or create blind spots.

Alerts and Cyber Event Detection

Hallucinations can also affect alerts and threat intelligence. Cyber news, vulnerability disclosures, and dark web activity create constant noise. AI can help reduce this workload by monitoring suppliers and classifying alerts based on relevance, credibility, and potential impact. But when AI misattributes news, flags irrelevant threats, or links alerts to nonexistent assets, teams waste time chasing false leads. 

AI Honeymoon Phase

When AI first entered TPRM workflows, it promised to transform a time-consuming and repetitive process. Teams were drowning in questionnaires, follow-ups, and manual evidence checks, and automation offered a faster path through these tasks.

Early adopters quickly recognized the benefits of AI, but the honeymoon period also exposed a critical limitation. Outputs may appear confident and complete, yet without verification, hidden errors can slip through. In TPRM, every decision must be backed by traceable evidence – especially when auditors, regulators, or internal stakeholders need to see exactly how a conclusion was reached.

An Overlooked Risk in AI: The “Confident Lie”

An AI hallucination is an output generated by an AI model that deviates from reality, lacks evidence to support its claim, and presents it as accurate. In third-party risk management, hallucinations often take the form of the “Confident Lie,” an answer that sounds correct even when there’s nothing to support it. 

When teams can’t tell which answers are verified, AI erodes trust and creates issues in security and compliance. With virtually every industry now using some form of automation, there have been lots of examples of AI hallucinations in practice. In Sydney, Australia, for example, a report to the Australian government included fabricated citations and phantom footnotes; as a result, the company that issued the report was forced to refund part of its contract to the government. This ended up being an over $60,000 mistake. 

In TPRM, an AI hallucination could create an incorrect record. Consider a standard TPRM workflow: An AI model scans a vendor’s security package and gives you the green light – ‘Yes, SOC 2 Type II is in place.’ But AI reads words, not context. It might miss that the report is actually just a Type I, or that it expired six months ago. Suddenly, you aren’t just dealing with a data error; you’re basing critical risk decisions on phantom assurance.

Why Generic AI Hallucinates 

In September 2025, OpenAI released a paper titled Why Language Models Hallucinate, noting that hallucinations aren’t just random bugs; they’re predictable outcomes of how most large-language models (LLMs) are trained and evaluated.

Generic LLMs are optimized to produce the next most likely words, and in many training setups, they’re implicitly rewarded for giving an answer rather than admitting uncertainty. When a model is “unsure,” guessing is presumed to be better than sharing what it doesn’t know, so the system learns to respond even when it lacks enough evidence.

Context blindness is another reason generic LLMs hallucinate. TPRM decisions are inherently situational: what’s “true” depends on which framework applies, the regulations in the region, and what has changed since the last assessment. A general model doesn’t reliably track those variables, so it generates answers that sound reasonable but miss key content. 

They also suffer from source amnesia. Even when an output sounds correct, the model can’t reliably show where the claim came from, whether it’s current, or whether an authoritative source supports it. The lack of traceability is a security and compliance gap, because being able to show evidence is often as important as the answer itself. 

AI hallucinations aren’t bugs that can be fixed with better prompts. They’re the predictable outcome of using a model that generates confident answers without built-in verification. This is a design limitation, not a misconfiguration. 

The Real Cost of Getting It Wrong

When AI outputs can’t be trusted, organizations pay in time, money, and customer confidence. This is the rework tax of AI-assisted TPRM: double-checking every suggested answer, re-reading documents, confirming detected cyber events and validating claims externally because the output might be wrong. Instead of shortening assessments, AI adds a layer of review, typically creating even more work than manually completing the task.

The impact goes beyond efficiency. Regulations like DORA and NIS2 raise expectations for third-party evidence that is traceable and verified. In audits or regulatory reviews, teams must show how conclusions are reached and what evidence supports them. If an AI-generated response can’t be tied back to a verifiable source, organizations end up standing behind claims they can’t defend. 

Then there’s the risk of quiet mistakes. Unvalidated AI can surface alerts that don’t matter, apply the wrong details to the wrong vendor, or even point to the wrong asset. These mistakes don’t always appear as failures, but over time, they create false confidence, pull teams away from real problems, and make it harder for people to trust AI results. 

Security questionnaires aren’t just a formality in TPRM. They surface critical details about a vendor’s security position so teams can ensure the organization is operationally secure. If an AI hallucinates an answer and a vulnerability is incorrectly marked as addressed, security practitioners might act  based on false confidence, leaving an exposure in place until it’s discovered in a breach. 

The Shift to Unified Collective Truth

AI has undeniably solved various issues in TPRM – scaling vendor coverage, filtering the noise by prioritizing cyber alerts and crunching evidence at a velocity manual teams simply can’t touch. But speed without accuracy is just a faster way to fail. The industry conversation has shifted and it’s no longer about whether to adopt AI, but how to operationalize it without trading your program’s assurance for efficiency.

That’s why Panorays developed a multi-source approach to AI-generated responses. Rather than relying on a single model’s conclusion, responses are grounded in multiple independent sources, ensuring teams can deliver results faster without losing the ability to verify and defend outputs. 

Validated AI in TPRM looks like: 

  • Auto-filled answers rely on multiple sources and are linked to specific evidence.
  • Validating answers for reviewals helps in flagging conflicts and missing information immediately, rather than guessing.
  • Alerts are prioritized, so teams can focus on producing accurate results.
  • External signals such as cyber news, threat intelligence, and dark web are filtered for relevance and confidence.

Panorays demonstrates this approach by using AI specifically built for TPRM, designed to validate answers and cyber insights rather than generate generic information.

Hallucination-Resistant AI Across TPRM Questionnaires

Panorays’ Smart Match automation engine demonstrates how AI can accelerate TPRM workflows without creating hallucinations. By grounding outputs in evidence across multiple sources, it helps teams act quickly while staying confident in their decisions.

Internal Truth: Panorays uses natural language processing (NLP) to compare each question against the organization’s existing knowledge base of previously answered questionnaires, helping suggest AI responses to align with what has already been validated and used. 

Public Truth: Panorays leverages Google Gemini’s double-search to auto-fill questions against live, publicly available sources. This validates statements against live sources rather than relying on assumptions or outdated information. 

Certified Truth: Panorays also uses Gemini to extract and verify details from certifications and attestations, so suggested answers reflect what the documents actually support.

By combining insights across these three pillars, Panorays reduces false confidence and blind spots. Whether autofilling questionnaires, mapping vendor relationships, or prioritizing cyber alerts, outputs are traceable, accurate, and defensible, minimizing rework and ensuring risk decisions are grounded in reality.

The goal isn’t to reject AI; it’s safe use. As more risk teams rely on automation for day-to-day work, outputs must be defensible. With the right tools, AI can accelerate TPRM processes without increasing risk. 

AI You Can Defend

AI isn’t the problem in TPRM – unvalidated AI is. Without verification, AI outputs can hallucinate answers, misattribute risks, or surface irrelevant cyber threats, creating false confidence and hidden blind spots.

With the right tools, teams can leverage AI across all relevant workflows for reviewing documents, extracting key details, flagging gaps, validating vendor relationships, and filtering cyber signals – all while anchoring every output to evidence. Validation-first automation ensures that insights are not only fast but also accurate and traceable.

Panorays is a real-world example of multi-source, hallucination-resistant AI in action. Its platform accelerates workflows while reducing risk of false confidence, whether filling questionnaires, mapping supply chains, or prioritizing cyber alerts. In TPRM, the goal isn’t just speed. It’s speed without suspicion, workflows that help teams complete tasks more efficiently while staying confident in what they submit.

See how Panoray’s multi-layered AI suite supports faster, more defensible TPRM.