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Key Insights: Agentic AI offers potential for third-party risk management by helping firms handle vendor assessment at scale, something manual processes struggle with as relationship counts grow. AI tools themselves also become third parties worth evaluating carefully, including their data handling, model accuracy, and security practices.
Third-party relationships have become a growing source of security exposure. Verizon's 2025 Data Breach Investigations Report suggests vendors now account for around 30% of data breaches, roughly double the rate from previous years. The challenge isn't necessarily that individual vendors pose more risk than before; it's that most organizations now manage far more relationships than their assessment processes were designed to handle. A quarterly review cycle that worked well for twenty key vendors starts to strain when that number reaches two hundred.
Audit and advisory firms feel this pressure acutely. Partners helping clients navigate vendor risk need ways to evaluate large populations systematically, track how risk profiles shift over time, and document their oversight in ways that satisfy both boards and regulators. When each assessment requires substantial practitioner time, capacity becomes the limiting factor.
Agentic AI platforms offer one path forward, helping automate portions of vendor assessment, supporting more frequent monitoring, and standardizing how firms evaluate controls across different frameworks. This article examines how AI is reshaping TPRM delivery, the regulatory drivers creating capacity constraints, professional standards governing AI adoption, and practical implementation strategies.
How Agentic AI Changes TPRM Delivery
The volume problem in third-party risk management is well understood: there are simply more vendors to evaluate than most teams can assess manually with any depth. Agentic AI platforms approach this by handling some of the repetitive analytical work that traditionally consumed practitioner hours.
Natural language processing can review vendor contracts and extract key terms, flag potential compliance gaps, or identify provisions that warrant closer legal review. Machine learning models can score vendors based on patterns in historical incident data, public breach disclosures, and financial indicators. These capabilities don't replace professional judgment, but they can surface information faster than manual review and help practitioners focus attention where it matters most.
From Periodic Reviews to More Timely Monitoring
One of the more meaningful shifts involves monitoring frequency. Traditional TPRM programs often rely on annual or quarterly reviews, which means a vendor's security posture might change significantly between assessment cycles without anyone noticing.
Where reliable external data sources are available and integrated into firm workflows, some AI platforms can track various indicators on a more continuous basis, including security ratings, financial stability signals, regulatory actions, and breach disclosures, then alert practitioners when something warrants attention.
Whether this represents genuine improvement depends heavily on data quality and how well the alerts integrate into existing workflows.
Streamlining Vendor Assessment
The intake process for new vendors tends to consume disproportionate time relative to its complexity. Platforms can reduce some of this burden by pre-filling questionnaires with historical data or publicly available information, extracting standard contract terms automatically, and scoring responses against control frameworks.
The goal isn't to eliminate practitioner involvement but to shift their attention toward non-standard provisions rather than routine data gathering.
Evaluating AI Tools as Third Parties
There's an inherent irony worth acknowledging: AI tools that analyze sensitive vendor information become third parties themselves. Firms should apply the same evaluation rigor to their AI vendors that they'd expect clients to apply elsewhere, examining data handling practices, model accuracy, security controls, and responsible AI governance. The tool meant to manage third-party risk shouldn't become an unexamined source of it.
Why Third-Party Risk Creates Capacity Constraints
Regulatory expectations have evolved considerably, and this partly explains why traditional approaches feel increasingly strained. OCC Bulletin 2023-17 articulates what many regulators now expect: lifecycle management spanning planning, due diligence, contract negotiation, ongoing monitoring, and termination. The days of assessing vendors once at onboarding and filing away the documentation have largely passed.
NIST Cybersecurity Framework 2.0 added dedicated supply chain provisions extending security expectations across vendor ecosystems, while IIA Third-Party Requirements established baseline standards for internal audit oversight. These frameworks layer on top of each other: continuous monitoring rather than periodic snapshots, documented evidence across control domains, and consistent standards regardless of vendor count.
For firms delivering TPRM services, this creates a genuine tension. Meeting these overlapping requirements takes time, and practitioner hours remain finite. When each vendor assessment demands substantial effort for questionnaire review, control validation, and documentation, the math eventually stops working. Something has to give, whether that's the depth of assessment, the number of relationships covered, or the approach itself.
Professional Standards Governing AI in TPRM
Professional standards haven't ignored the rise of AI in audit work, though the guidance tends toward principles rather than prescriptive rules. This makes sense given how quickly the technology evolves, but it also means firms need to interpret how existing standards apply to new tools.
Key Framework Requirements
AICPA quality management standards set a straightforward bar: documentation should be comprehensive enough for an experienced reviewer to understand and replicate the work. This standard doesn't change just because AI performed some of the analysis. Firms still need to document tool selection, how they configured it, and what the outputs looked like.
IIA guidance takes a broader view, emphasizing that effective AI governance builds stakeholder trust over time. Internal audit teams evaluating TPRM programs should assess governance structures, human oversight mechanisms, and ethical considerations alongside technical controls. The same criteria apply when audit and advisory firms adopt AI tools themselves.
ISACA's COBIT framework offers governance objectives organized around eight elements of trustworthy AI: transparency, accountability, fairness, privacy, reliability, safety, resilience, and security. These provide useful categories for thinking through AI adoption, even if the specific implementation varies by firm and use case.
Considerations for Responsible Adoption
Several recurring themes emerge across this guidance. Confidentiality matters when AI platforms process client data or sensitive vendor information. Reliability remains an open question since AI tools can produce incorrect outputs, and professional standards expect practitioners to evaluate that risk. Context is another consideration: AI may miss nuances that experienced practitioners would catch immediately.
None of this means firms should avoid AI adoption. It does suggest that appropriate controls aren't optional extras.
What Clients Expect
Client expectations tend to reinforce this balanced approach. Companies generally want auditors using AI for risk mitigation and internal controls work, but they also expect firms to explain how technology improves risk identification without diminishing the professional skepticism they're paying for.
The firms that navigate this well will likely be those that can articulate clearly where AI accelerates work and where it doesn't.
Building Framework Integration
Most clients don't face just one compliance framework. SOC 2, ISO 27001, NIST, and others often overlap significantly in their control requirements. AICPA's framework mapping shows approximately 80% of SOC 2 criteria align with ISO 27001, with overlap in common security domains such as access control, incident management, and risk assessment. This creates an opportunity: rather than treating each framework as a separate assessment process, firms can build unified TPRM programs around common control sets.
AI platforms can support this integration by standardizing how evidence gets collected and organized. When practitioners configure requirements once and apply them across vendor populations, they avoid duplicating work across frameworks that essentially ask the same questions in different languages.
That said, the risk-based approach underlying good TPRM work doesn't change just because the tools get smarter. OCC Bulletin 2023-17 makes clear that oversight intensity should match vendor criticality and risk level. AI scoring models can help categorize vendors and surface potential concerns, but practitioners retain authority over final risk ratings and how much oversight any particular relationship warrants.
Implementation Strategy for Audit and Advisory Firms
Firms that see meaningful results from AI-powered TPRM tend to invest in change management before they invest heavily in technology. Deloitte research suggests organizations prioritizing change management are roughly 1.6 times as likely to report that AI initiatives exceed expectations, yet only about 37% report making significant change management investments. That gap probably explains why capable platforms sometimes deliver underwhelming results.
Redesign Workflows, Don't Just Automate Them
When teams encounter bottlenecks in vendor management, the instinct is often to digitize whatever process already exists. But the firms getting real value tend to step back and ask whether the workflow itself makes sense. Sometimes the answer is reconfiguring the entire approach: moving from sequential document requests to parallel automated evidence collection, for example, rather than just making the sequential process slightly faster.
Rethinking Practitioner Roles
Technology adoption works better when roles evolve alongside it. Practitioners can shift toward risk interpretation, client advisory work, and handling the exceptions that require genuine expertise. AI handles more of the routine data collection, preliminary scoring, and monitoring. The team as a whole develops enough data fluency to understand how scoring models work and when their recommendations need a second look.
Without this kind of intentional role evolution, firms sometimes find that practitioners either ignore AI outputs entirely or accept them uncritically. Neither outcome justifies the investment.
Starting Small
Most successful implementations start with controlled scope: perhaps a pilot on lower-risk vendor populations or a specific framework assessment. This gives teams time to develop expertise, refine how the tools integrate into their workflows, and demonstrate concrete value before expanding to more critical vendor relationships or broader practice areas.
Market Growth and Strategic Priority
The TPRM market has been growing steadily, and the underlying drivers aren't hard to identify. Gartner points to what they call a "perfect storm": trade volatility, persistent cyberattacks, expanding regulatory requirements, and supply chain disruptions that keep reminding organizations why vendor oversight matters. These factors push clients toward more systematic TPRM programs, and firms with mature capabilities find it easier to differentiate in competitive situations.
The technology side remains relatively early stage, even with all the investment activity. Big Four firms have made substantial AI commitments, but detailed public case studies showing specific TPRM efficiency gains remain uncommon, likely because implementations are still maturing. For mid-market firms, this timing could represent opportunity. Investing in agentic AI capabilities now, before client expectations fully crystallize, might establish advantages that become harder to build later.
Getting Started With AI-Powered TPRM
Effective TPRM at scale requires platforms designed for how audit and advisory firms actually work, not generic vendor management tools built for corporate procurement. The requirements differ meaningfully: corporate teams track contracts and performance metrics, while practitioners need structured documentation, examination standards, and framework compliance across multiple concurrent engagements.
Fieldguide's engagement automation platform supports TPRM delivery with workflows designed for professional services requirements.
The Engagement Hub helps partners track status across concurrent work, and pre-built framework support for SOC 2, ISO 27001, and NIST reduces duplicate effort. Field Agents assist with controls testing by automating defined testing steps within practitioner-set parameters, producing structured, auditable documentation. The goal is helping firms serve growing TPRM demand without proportionally growing headcount. Request a demo to explore whether it fits your practice.
Amanda Waldmann
Increasing trust with AI for audit and advisory firms.
