Most third-party risk programs were built around a reasonable assumption: a vendor assessed in March behaves roughly the same way in September.
That assumption held for years. It holds far less well now. A questionnaire captures a snapshot of something that will not hold still, and by the time an AI-enabled vendor finishes answering, the model behind the product has changed, a new subprocessor has appeared, and last quarter’s data handling terms have already moved on.
Scaling oversight across hundreds or thousands of vendors is usually framed as a volume problem. Add more reviewers, add more spreadsheets, add a platform. Volume is real, though the harder problem underneath is volatility. A program designed to assess a stable thing periodically struggles when the thing changes between assessments.
Fixing that requires rethinking how risk data gets collected, standardized, and acted on, rather than simply doing more of what already runs.
Why Does Vendor Growth Outpace Manual Oversight?
Manual review cycles scale linearly. Vendor ecosystems do not.
Most programs start with questionnaires, periodic reviews, and email follow-ups. That works at a few dozen vendors. Past a few hundred, new relationships, contract renewals, and shifting regulatory requirements arrive faster than a team can process them.
The gap that opens is uneven rather than uniform. Some vendors get thorough scrutiny because somebody onboarded them early or flagged them at the outset. Others drift into blind spots as attention moves elsewhere. Inconsistent oversight creates roughly the same exposure as insufficient oversight, and it is harder to spot from inside the program.
Effective enterprise third-party risk management at this scale depends less on review capacity than on deciding what deserves review in the first place.
What Changed About Vendor Risk?
Three things, all recent.
Vendors acquired dependencies you never assessed. AI vendors train models on customer data, produce outputs that shape business decisions, and often rely on subprocessors and foundation model providers sitting several layers removed from the contract anyone signed. A clean SOC 2 on the vendor tells you little about the model provider behind it.
Procurement stopped being the entry point. Business teams select AI tools on capability and speed to deploy, with security and privacy review arriving later or getting deprioritized entirely. The vendor is in production before the risk team learns its name.
Oversight programs sit apart from the rest of governance. TPRM frequently operates separately from broader GRC, and disconnected tooling makes it difficult to keep risk records current or trace how vendor risk touches the wider control environment.
None of that argues against structure. It argues that the structure needs to detect change rather than record state.
How Should Vendors Be Tiered?
By potential impact, not by contract value or onboarding date.
Tiered classification remains the most effective way to bring order to a sprawling vendor base. A payroll processor handling employee data warrants different treatment from an office furniture supplier, even though both sit in the same vendor table.
A workable tiering model weighs several factors:
- The type and sensitivity of data the vendor accesses or processes
- How deeply the vendor integrates with core systems or infrastructure
- Regulatory and contractual obligations attached to the relationship
- The vendor’s own security posture and incident history
- Business impact if the vendor experienced a disruption
Two factors deserve adding for AI-enabled vendors. Whether the vendor can document its data provenance, and whether it can name its subprocessor chain. Vendors unable to answer those questions arguably belong in a higher tier by default, since undocumented provenance and opaque subprocessor chains are among the risk factors most likely to surface during an incident.
Once tiered, review frequency, assessment depth, and monitoring resources follow proportionally. High-tier vendors get continuous monitoring and detailed periodic assessment. Lower tiers move through lighter automated cycles.
Why Does Standardized Intake Matter More Now?
Because comparison is impossible without it, and comparison is how you spot drift.
When business units run their own onboarding with different questionnaires, criteria, and documentation standards, the resulting data resists aggregation. That fragmentation stalls more scaling efforts than any technology gap.
Standardized intake built around common frameworks such as SOC 2, ISO 27001, or NIST guidelines creates a comparable baseline across the population. Risk teams can then rank vendors against each other, identify patterns, and direct attention using actual indicators rather than accumulated impressions.
Vendors benefit too. Most respond to many clients, and consistent, well-structured requests cost them less than a bespoke questionnaire per relationship.
Centralizing that data matters as much as standardizing it. Without a single source of truth, visibility fragments along the same lines as internal silos, which defeats the purpose of the exercise.
What Should Continuous Monitoring Actually Watch?
Change signals, routed by materiality rather than volume.
Point-in-time assessment captures one moment. Security postures, ownership structures, and compliance statuses shift faster than an annual cycle catches, leaving gaps that persist for months.
Continuous monitoring tracks security ratings, breach disclosures, financial health indicators, and regulatory actions on an ongoing basis. For AI-enabled vendors, the watchlist extends further: changes to vendor policies, subprocessors, and the underlying models themselves.
Automation makes this feasible at scale, and it is where AI genuinely earns its place in the program rather than merely creating work for it. Systems can review questionnaires and evidence packages faster than people, prioritize findings by criticality and impact, map vendor risk to internal controls and regulatory requirements, and trigger human reassessment when something material surfaces.
That shifts the risk team from hunting for problems to adjudicating prioritized ones, which is the only version of this job that survives a growing vendor base.
On the frequently repeated claim that continuous monitoring detects issues earlier than periodic review: the direction is well supported, though precise figures vary widely between studies, and headline numbers in this space often obscure how they were measured. Treat the pattern as reliable and the specific percentages with care.
What About Vendors That Act Rather Than Store?
A newer category, and existing frameworks handle it poorly.
Traditional third parties hold data or process transactions. An agentic vendor holds credentials and takes actions inside your environment, on a schedule nobody watches in real time.
Vanta’s State of Trust Report found 65% of organizations saying their agentic AI use outpaces their understanding of it, which is a governance gap rather than a technology gap. The controls follow from treating the agent as an actor with permissions rather than a service with a data processing agreement.
The infrastructure question arrives alongside it. Agents generate request patterns at volumes and intervals that break assumptions built into older network security models, so monitoring designed around human access patterns may not flag anything unusual.
Organizations handling agentic deployments well build fixed review points into the workflow, the approach government agencies took when rolling out agentic AI rather than granting open autonomy. Vendor-supplied agents deserve the same treatment.
How Do You Address Shadow AI in the Vendor Base?
Discovery first, since you cannot tier what you cannot see.
The cost is measurable. IBM’s 2025 Cost of a Data Breach Report found organizations with high shadow AI exposure paid around $670,000 more per breach than those with little or none. The same report found 97% of organizations suffering an AI-related security incident lacked basic AI access controls, and 63% had no AI governance policy at all.
The proliferation is structural rather than a discipline problem. Niche AI tools now cover tasks general assistants never handled properly, which means a marketing analyst finds a purpose-built tool for their exact workflow and adopts it in an afternoon. No procurement process moves at that speed.
Three practical responses:
- Run periodic discovery across expense reports, SSO logs, and network traffic to find what is already in use
- Build a lightweight fast-track intake for low-risk tools, so the sanctioned path is quicker than the unsanctioned one
- Set clear data-handling rules employees can apply without consulting anyone
Blocking alone tends to push usage further out of sight.
Who Owns This Across the Organization?
Procurement, legal, IT security, and business unit leaders, with defined decision rights.
Scaling third-party risk is not solely a technology or process problem. Without clear accountability, assessments drift away from the people managing day-to-day vendor relationships, which degrades both data quality and the ability to act on findings.
Effective programs define escalation paths in advance. Who reviews a finding, who decides on remediation, and who holds authority to pause or terminate a relationship. Settling those questions during an incident wastes the time the monitoring bought you.
Distributing responsibility while keeping data centralized and standardized tends to strike the right balance. No single team maintains complete visibility into every relationship, however well resourced it is.
FAQs
Q. What makes scaling TPRM difficult beyond vendor count?
Rate of change. Vendor risk profiles now shift between review cycles, particularly where AI features, model providers, or subprocessors are involved.
Q. How should AI vendors be assessed differently?
Add questions on training data provenance, model change notification, subprocessor disclosure, output accountability, and how the system evolves after deployment. Standard software questionnaires cover none of that.
Q. Does continuous monitoring replace periodic assessment?
No. Monitoring catches change between assessments. Assessments establish the baseline that makes a change meaningful.
Q. What is shadow AI and why does it matter for vendor risk?
Employee use of AI tools without security approval. It creates vendor relationships nobody assessed, and it carries a measurable breach cost premium.
Q. Where should a program start if it is currently manual?
Tiering and standardized intake. Automation applied to inconsistent data produces confident nonsense at scale.
Q. How often should high-tier vendors be reviewed?
Continuously for change signals, with full assessment at a defined cadence. Frequency matters less than whether anything triggers a review between scheduled ones.
The Bottom Line
Scaling third-party risk across a large vendor ecosystem is less about adding reviewers and more about building a program that stays consistent as volume grows.
Tiering directs attention. Standardized intake makes the data comparable. Continuous monitoring covers the space between assessments. Cross-functional ownership keeps accountability distributed rather than stuck in one team.
None of it works alone. Tiering without standardized data produces inconsistent inputs. Monitoring without clear ownership generates alerts nobody closes.
The programs that hold up treat these as one system, and they design it around a vendor base that keeps moving rather than one that sits still between reviews.
Related: The Business Case for AI in Healthcare: Costs, ROI, and Smarter Adoption
