AI Risk Recommendations for Vendors | SAFE
Remediation

AI-Driven Risk Decisioning
and Recommendations

SAFE TPRM brings all of a vendor's risk evidence together, explains what drives the risk, flags conflicting claims, and recommends the next action for your team to approve.

The Problem

More evidence does not mean better decisions

  1. Evidence is reviewed in pieces

    Questionnaires, documents and external findings are read separately, so contradictions between them go unnoticed.

  2. The drivers of risk are unclear

    A rating alone does not tell an analyst which gaps matter most or what to fix first.

  3. Decisions vary by analyst

    Similar vendors can get different conclusions depending on who reviews them.

  4. Unexplained AI is hard to defend

    A recommendation without its reasoning cannot be explained to an auditor, a regulator or leadership.

How It Works

Evidence in, explained recommendation out

SAFE synthesizes assessments, findings, controls, external exposure, documents and threat intelligence for each third party, identifies what contributes most to its risk, and suggests the next step with the evidence behind it.

Risk decisioning is part of the Remediation stage of the SAFE TPRM lifecycle.

Third-party AI summary combining assessment, document and outside-in evidence

Bring every risk signal into one view

Assessments, findings, control gaps, external exposure, documents and threat intelligence combine into one view of each third party.

  • An AI summary gives analysts and stakeholders a readable overview of the vendor's risk.
  • Teams see the full picture without opening every record.
  • Evidence comes from document and evidence intelligence and the rest of the SAFE TPRM lifecycle.
Conflict view linking a vendor answer to a contradicting SOC exception and outside-in finding

Detect contradictory claims across responses, evidence and external intelligence

SAFE compares questionnaire responses, supporting documents, assessment findings and external signals to find information that does not agree.

  • Mismatches are flagged, such as controls that conflict with SOC exceptions, expired certifications or outside-in observations.
  • Each conflict is explained with linked source evidence, so analysts can validate it without repeating the review.
  • Conflicts are prioritized by their potential risk impact.
Top Control Recommendations and Top Outside-In Recommendations for a third party

See what drives each vendor's risk

SAFE surfaces the factors that contribute most to each third party's risk.

  • Control and outside-in recommendations show where improvement would matter most.
  • Recommendations are ranked so the order of work follows the risk.
  • An AI narrative explains why a score changed, so analysts can tell leadership what moved and why.
Why SAFE

AI recommendations your team can defend

Grounded in real risk evidence

Recommendations come from each vendor's findings, control gaps, assessments and external exposure instead of generic AI reasoning.

Explainable by design

Every recommendation links back to the underlying evidence and risk drivers, so analysts see exactly why action is suggested.

Human-governed

AI-generated issues and recommendations are suggestions, and analysts approve material actions, remediation decisions and changes to risk.

Improves with analyst feedback

Reviewer feedback on AI assessments and recommendations is used to evaluate quality, find gaps and improve accuracy over time.

The Proof

Machine-Speed Defense. Measurable Results.

Instacart operationalizes enterprise-scale TPRM while reducing assessment effort.

Retail USA

Read the Full Story

The Challenge

Instacart's TPRM team managed hundreds of vendors through manual processes that slowed assessments and limited visibility. They needed a more automated, risk-based approach to scale the program.

The Results

30+
hours
of analyst work eliminated per assessment
3
weeks
to operationalize TPRM with 600+ vendors
100%
vendors
assessed and monitored with zero added headcount
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