95% faster risk assessment across 250+ third-party AI vendors

A centralized platform for monitoring, assessing, and managing the risk posed by external AI vendors, combining automated detection, AI-powered assessment, human-in-the-loop decisions, and a real-time operational dashboard.

Outcome

250+ AI vendors under continuous watch, vendor risk assessment down from two to three weeks of manual review to minutes of AI analysis plus hours of human review, and a structured audit trail of every decision for compliance reporting.

See the results

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At a glance

Client & industry

Global technology and digital platform company, with AI tooling in use across engineering, content, marketing, and operations teams

Engagement

Fixed scope, design and build a Third-Party AI Risk Management (3P-AIRM) platform, from problem discovery and architecture through production rollout

Scale

250+ third-party AI vendors in active use, assessed across data privacy, security, compliance, operational, and financial risk

Regulatory context

EU AI Act, Digital Services Act, and GDPR obligations covering every third-party AI vendor that processes client data

Business owner

The client's vendor strategy and operations function, working alongside security, legal, procurement, and engineering

Maps to below services

Agentic AI & AgentOps, AI & ML Engineering, Data Engineering & Advanced Analytics, Cloud & Platform Engineering

Where it started

A governance blind spot opening up as AI adoption accelerated across the enterprise, ahead of any central system for tracking external AI vendors.

Business problem

Teams across the business adopted third-party AI tools on their own, spanning generative image and audio tools, AI coding assistants, and other categories. Leadership could answer none of the basic governance questions: which vendors are in use, by whom, and what data they access.

Lack of decision framework

Each review ran manually and took two to three weeks, so the backlog grew faster than the team could clear it. Approvals, rejections, and remediations were handled case by case, the audit trail lived in email, and a change in a vendor's risk profile, a breach or a contract deviation, surfaced only after the fact.

Agivant's role

Architecture, agent engineering, workflow design, and production rollout across detection, assessment, decisioning, and remediation

What Agivant built

Forward Deployed Engineers embedded with the vendor strategy and operations function through problem discovery, architecture, workflow design, and production rollout. Risk signals arrive continuously, an LLM drafts the assessment, an analyst rules on it through the dashboard, and the workflow engine executes the approved action. Value in days and weeks.

Automated risk detection

Continuously monitors vendor risk signals pulled from multiple data sources, raising an alert as each one appears.

AI-powered assessment

An LLM scores severity, proposes a remediation path, and recommends an owner, turning a raw alert into an actionable recommendation.

Human-in-the-loop decisions

Analysts review every AI-generated recommendation through an interactive dashboard and can approve it, reject it, or send it back for replanning.

Iterative replan loop

On a replan request the AI agent revises its recommendation and its reasoning, for as many rounds as the analyst needs.

Automated remediation orchestration

Once a decision is approved, the workflow engine executes the remediation action automatically.

Audit and compliance logging

Every decision, human and AI, is captured in a structured log built to support regulatory and compliance reporting.

Real-time notifications

Risk owners and stakeholders are alerted the moment a decision or an action is needed.

Error handling and escalation

Failed actions are caught, logged, and escalated along a defined path, so nothing disappears silently.

How it is built

Architecture	Workflow engine	Dashboard	AI risk scanning	Data store	Production workflows	Notifications	Security	Governance controls
Three layers, data, orchestration, and presentation, each able to evolve independently	n8n, orchestrating risk assessment, decision routing, and remediation execution	Next.js, the interactive human-in-the-loop interface for risk analysts	PromptArmor, for third-party AI security scanning and vulnerability detection	Enterprise data warehouse tables plus a configuration sheet, holding alert logs, decision logs, and error tracking	Orchestrator as the HITL decision router, executor as the remediation engine	Chat API integration, alerting risk owners in real time	JWT (HS256) signed webhooks, a secure reverse proxy for external API calls, keychain-managed secrets	Analyst approval required on every action before it executes

Before

A manual, ad hoc review process tracked in email, with each function keeping its own separate view of vendor risk.

After

An agent layer feeding AI-drafted assessments into the analyst dashboard, approved decisions executing and logging automatically, and one shared governance record across security, legal, procurement, and engineering.

Proof in production

An alert reaches an analyst carrying vendor, severity, priority, risk category, a plain-language summary, the AI confidence score, a remediation path, an owner, an effort estimate, and an SLA. The analyst approves, rejects, or requests a replan, and the agent returns a revised plan with its reasoning. Approved decisions route to the executor, which runs the action, logs it, and notifies stakeholders.

Measured results

The same team of analysts, using the same judgment they always brought to vendor risk, now covers 10x the ground because the AI does the first-pass analysis and the workflow engine handles execution.

~95% down

Vendor risk assessment time

5 to 50+

Assessments per analyst each week

~70% faster

Mean time to remediate

100%

Risk visibility coverage

250+

Third-party AI vendors monitored

The repeatable pattern

Reusable asset

A reusable HITL pattern, AI proposes, human decides, system executes, that extends to any governance domain needing AI-assisted recommendations with human accountability, and lays the foundation for an AI Consumption Control Tower across all external AI usage. Every build amplifies the next.

Challenges overcome

Keeping a human ruling on every decision while clearing the bottleneck the manual process had created. The AI first pass had to arrive complete enough for an analyst to act in minutes, and the replan loop covers the cases that need a second look before anything executes.

Enterprise movement

Foundational

Operational

Path to autonomous