AI due diligence software for third parties

AI due diligence software helps check a third party’s reputation, sanctions exposure, lawsuits and financial health in far less time than a manual review, and hands the analyst a prioritised risk picture. If you are choosing a tool now, what separates a good platform from a showcase of dashboards is traceability: every finding must point to its source, and the decision to approve or reject the supplier must stay with a person.

The pressure to get these checks right keeps growing. In the Verizon 2026 Data Breach Investigations Report, released in May 2026, breaches involving a third party reached 48% of the total, up 60% from the previous edition, which had already recorded a doubling to 30% (Verizon DBIR 2025). Every new supplier is a door into your environment, and the onboarding spreadsheet that took weeks to close has become a risk bottleneck.

What due diligence is and when to apply it to partners and suppliers

Third-party due diligence is the structured investigation of a partner before signing a contract and throughout the relationship. It answers three practical questions: is this third party who it claims to be, does it pose a legal or reputational risk, and does its security posture compromise yours.

In practice, the checks cover:

Identity and corporate structure: who the shareholders, controllers and ultimate beneficial owners are.
Sanctions and PEP lists: exposure to sanctions programmes and to politically exposed persons.
Reputation and adverse media: lawsuits, convictions, negative news and regulatory history.
Financial health: the risk of the supplier going out of business and the impact on your operations.
Security and privacy posture: certifications, controls and alignment with the LGPD, Brazil’s General Data Protection Law.

The most obvious trigger is onboarding a new supplier. But due diligence also applies to mergers and acquisitions, to taking on distributors and sales agents, to renewing critical contracts and whenever a third party starts processing personal data or accessing sensitive systems. Depth should vary with risk: an office supplies vendor does not need the same scrutiny as a payment processor.

In Brazil, this logic also appears in regulation. Decree No. 11,129/2022, which implements the Anti-Corruption Law (Law No. 12,846/2013), lists among the criteria for assessing a compliance (integrity) programme “appropriate, risk-based due diligence” for hiring and, where applicable, supervising third parties and politically exposed persons (art. 57, XIII), as well as checks for wrongdoing and vulnerabilities in mergers, acquisitions and corporate restructurings (art. 57, XIV). For financial institutions, CMN Resolution No. 4,893/2021, issued by Brazil’s National Monetary Council, requires procedures proportionate to the relevance of the service when contracting data processing and cloud services, the topic of our article on third-party risk and CMN Resolution 4,893.

Professional analysing risk and compliance data on a digital dashboard

How AI speeds up reputation, sanctions and legal risk checks

Manual checks are slow because the analyst has to query dozens of scattered sources one by one: official gazettes, sanctions lists, courts, company registries and the media. For each third party, that means hours of searching and a constant risk of missing something.

AI changes the pace because it runs those queries in parallel. In a single pass, it matches the name of the third party and its shareholders against sanctions databases such as the SDN list of OFAC, the US Treasury’s Office of Foreign Assets Control, scans adverse media in several languages and groups lawsuits by relevance. The result arrives as a prioritised summary rather than a pile of links: what needs immediate attention sits at the top, with the source excerpt attached.

That gain matters financially when checks fail. US sanctions enforcement jumped in 2025: OFAC penalties and settlements totalled about US$265.7 million across 14 cases, against US$48.8 million across 12 cases in 2024 (OFAC, 2025; OFAC, 2024). Much of the jump comes from a single settlement of about US$216 million. Automating screening does not remove human judgement, but it reduces the chance that a critical source goes unchecked for lack of time.

The balance point is the human-in-the-loop model. AI helps reduce false negatives (the risk nobody saw) and organise false positives (the alert that looks serious but is not), while the analyst decides. Adoption is still maturing: in the KPMG Global Third-Party Risk Management Survey 2026, with 851 participants, between 50% and 58% of companies say they use AI in third-party risk processes, but only 22% rate that use as “very effective”. The outcome depends as much on how the tool is deployed as on the technology itself.

Automating document collection and analysis

The most thankless part of due diligence is the back-and-forth of documents. The third party sends articles of incorporation, good-standing certificates, security policies, a SOC 2 report and a security questionnaire, and someone has to read everything, extract the data and enter it into the system.

AI can automate much of that chain in three steps:

Collection: the supplier receives a document list tailored to its assigned risk level, with automatic reminders and format validation at upload.
Extraction: reading models pick out relevant fields in each document, such as certificate expiry dates, the scope of an ISO/IEC 27001 certification or exceptions in an audit report.
Analysis: the platform compares what was sent with what was required, flags gaps and points out inconsistencies, for example an expired certificate or a questionnaire that contradicts an attached policy.

Better data quality is what supports everything else. In the same KPMG survey, only 17% of participants say they have completely reliable, consistent and integrated third-party risk data. When collection is manual, information gets lost between emails and spreadsheets. When evidence is captured, versioned and linked to the relevant control, an audit stops being a document hunt. This is where a platform such as Futture’s Eagle comes in: its third-party risk module brings together the supplier register, assessments with criticality classification, contracts, periodic reviews and risk signals, and the AI suggests priorities and flags inconsistencies, with human review of every relevant result.

Continuous versus one-off due diligence

One-off due diligence is a snapshot taken at the time of contracting. The problem is that risk does not freeze after signature. A supplier approved in January may be added to a sanctions list in March, suffer a data breach in June or lose a certification in September, and the old snapshot will say nothing about it.

Continuous due diligence swaps the snapshot for ongoing monitoring. In the background, the platform watches for changes in sanctions, new lawsuits, adverse news and shifts in the third party’s security posture, and raises an alert when something moves to a new level.

Dimension	One-off due diligence	Continuous due diligence
Timing	At contracting or renewal	Throughout the relationship
Risk view	Static	Dynamic, updated by events
Effort	Concentrated and manual	Distributed and automated
Response to events	Reactive, at the next cycle	Proactive, by alert

Monitoring every third party at the same level, all the time, would be unworkable without automation. The practical answer is segmentation: critical suppliers get continuous monitoring and frequent reassessment, while low-risk ones follow longer cycles. AI can support the triage that sets this cadence, but the criticality criteria belong to the team. It is the same proportionality principle that CMN Resolution 4,893 applies to data processing and cloud services: what sets the rigour is what breaks if the supplier stops.

Building AI due diligence into third-party onboarding

Checks only deliver value if they happen before the third party enters your operations, not after. When due diligence is a parallel step, done by email in a separate spreadsheet, high-risk suppliers end up hired while the review is still under way. Building AI into onboarding closes that gap.

An integrated flow works like this:

Initial registration: the third party fills in a form, and sanctions and reputation screening runs right after.
Risk classification: based on the findings and the type of service, the platform suggests a risk level that sets the depth of the checks.
Targeted collection: the list of documents and questionnaires is tailored to the risk, without asking the supplier for more than necessary.
Human review: the analyst receives the prioritised file and decides to approve, reject or approve with conditions.
Monitoring: approved third parties enter the continuous due diligence cycle that matches their criticality.

The detail that makes the difference is the approval gate. No supplier above a risk threshold moves forward in onboarding without a recorded human decision, together with the evidence behind it. That turns due diligence from a filed document into a living control, with an end-to-end audit trail. In regulated sectors, this traceability is what separates a mature GRC platform from a collection of disconnected tools.

Next step

Before comparing software vendors, map where your due diligence gets stuck today: in document collection time, in the depth of sanctions screening or in the lack of monitoring after signature. Pick one of your ten most critical suppliers, run a full check the way you do it now and time it. That number is your baseline. An AI due diligence tool is only worth the investment if it cuts that time measurably while leaving the final decision, and the evidence behind it, with your team.

Sources consulted on 28 October 2026: Verizon DBIR 2025 and 2026 (news releases), KPMG Global Third-Party Risk Management Survey 2026, OFAC (SDN list and 2024 and 2025 civil penalties), Decree No. 11,129/2022 and CMN Resolution No. 4,893/2021. This article is for information only and does not constitute legal advice.

Further reading: Vendor Risk Management: Best Practices.

Frequently asked questions

What is third-party due diligence?

It is the process of investigating a partner, supplier or distributor before and during the business relationship, checking reputation, legal standing, sanctions lists, financial health and security posture to size the risk it brings to the business.

How does AI speed up due diligence?

AI queries reputation, sanctions and litigation sources in parallel, extracts data from documents automatically and prioritises the most critical findings, leaving validation and the final decision to the analyst.

Does AI due diligence replace the human analyst?

No. In a human-in-the-loop model, AI handles collection and triage, but approving or rejecting the third party and interpreting the context remain a person’s responsibility, with a complete audit trail.

What is the difference between one-off and continuous due diligence?

One-off due diligence is a snapshot of the third party at the time of contracting. Continuous due diligence monitors changes in sanctions, litigation and security throughout the relationship and raises an alert when the risk changes.

AI Due Diligence Software for Third Parties