What are the best AI security and audit questions to ask during due diligence to maximize value?

Summary

• Effective AI due diligence must identify every deployed model and agent, map their data access, and require quality evidence built from real enterprise data rather than generic benchmarks.
• Agent sprawl across multiple models, clouds, and frameworks is a top governance risk, and Databricks addresses it through Agent Bricks, which provides a unified control plane with granular access controls, lineage tracking, and policy enforcement.
• Organizations should verify regulatory compliance with frameworks like the EU AI Act, NIST AI RMF, and ISO 42001, while assessing third-party vendor risk across the entire AI value chain.

AI security and audit questions to ask during due diligence

When evaluating an AI-driven company for acquisition, investment, or partnership, standard due diligence checklists fall short. Outcomes depend on data quality, model behavior, prompts, and third-party dependencies, not just code. A comprehensive AI risk management approach is essential to uncover what traditional audits miss.
The right questions expose hidden risks across governance, security, data lineage, and output quality. Below is a structured approach to asking those questions and turning answers into actionable risk assessments.

Three foundational questions every AI audit must answer

Anchor your due diligence around three questions:

1. Which AI agents and models exist? Identify every model, agent, and automation across the organization, including those built on different providers and frameworks.
2. What data do they access? Map data flows, permissions, and sensitive-data exposure from the model layer down to underlying data stores.
3. How well do they work? Require evidence of systematic evaluation built from real enterprise data, not demo results or generic benchmarks.

Why agent sprawl is the top governance risk

Teams adopt AI agents across multiple models, clouds, and frameworks, creating agent sprawl. This disorganized environment undermines security and governance, letting agents view confidential records or take unapproved, irreversible actions.
According to Gartner, by 2027, 40% of enterprises will demote or decommission autonomous AI agents due to governance gaps identified only after production incidents occur.
During due diligence, ask:

• Is there a centralized inventory of all deployed AI agents?
• Are granular access controls enforced per agent and per data source?
• Can the organization trace every agent action back to the data and model that produced it?

Agent Bricks (Mosaic AI Agent Framework) addresses this by providing a unified control plane to build, run, and govern all AI agents across any model, provider, or framework, with granular access controls, lineage tracking, cost controls, and policy enforcement.

How to evaluate governance, lineage, and access controls

Strong AI governance requires controls spanning data, models, and agent behavior.

Governance area	Key question	Red flag
Data lineage	Can outputs be traced to source data?	No version control on training data
Access controls	Are permissions enforced per agent?	Shared service accounts across agents
Policy enforcement	Are guardrails consistent across agents?	Manual, ad-hoc policy application
Audit logging	Are decisions logged with full context?	Logs missing model version or prompt

How to assess AI output quality and continuous improvement

Deploying AI agents without systematic evaluation creates brand risk. Incorrect responses can go undetected until real damage occurs.

• Are evaluation benchmarks built from real enterprise data?
• Is there a feedback loop connecting human reviewers to model improvement?
• Are there defined thresholds that trigger retraining or rollback?

Agent Bricks supports continuous quality improvement by building benchmarks from your own data and tasks, evaluating every output against them, and improving performance through prompt optimization, fine-tuning, RLHF, and human feedback.

What regulatory and compliance controls to verify

Key regulatory requirements to check include EU AI Act Annex IV documentation obligations, DORA Article 28 third-party register requirements, SOC 2 vendor management controls, and ISO 42001 AI management system controls. Verify these with auditable evidence, not just policy documents.

How to assess third-party AI vendor risk

Evaluate data suppliers, infrastructure providers, and model vendors using a whole-of-value-chain approach. Confirm training data provenance, licensing terms, IP ownership agreements, and multi-cloud flexibility to reduce lock-in.

FAQs

What key AI governance frameworks should be evaluated during due diligence for AI-driven companies?

Evaluate ISO/IEC 42001, the NIST AI Risk Management Framework, and the EU AI Act. Map programs to frameworks applicable in the organization's operating regions.

How do you assess the robustness of an AI model's training data pipeline during a security audit?

Verify data provenance, transformation lineage, and access controls at every stage. Ask whether data is used for training, how long it is stored, and who has access.

What are the most critical AI model risk factors to evaluate before an acquisition or investment?

Examine intellectual property rights, pending litigation, regulatory compliance, and algorithmic bias liability.

How should you evaluate AI bias, fairness, and explainability controls during due diligence?

Conduct structured bias audits analyzing datasets for representation gaps. Apply fairness metrics such as demographic parity or disparate impact.

What questions should you ask about AI data privacy compliance and regulatory readiness during due diligence?

Ask vendors to specify encryption methods, access controls, audit logging, penetration testing results, and incident response procedures.

How do you assess intellectual property ownership and licensing risks for AI models and training data?

Request training data provenance documentation, licensing terms for third-party datasets, and model ownership agreements with audit rights.

What are the essential questions to ask about AI model monitoring, drift detection, and retraining processes?

Define operational thresholds for acceptable performance. Retraining or rollback mechanisms should trigger automatically when metrics cross those thresholds.

How do you evaluate an organization's AI incident response plan and vulnerability management practices?

Ask for SLAs on uptime and latency, incident response times, status reporting, and post-incident review processes.

What audit controls should be in place to ensure AI outputs are traceable and reproducible in regulated industries?

Full lineage tracking from input data through model version to final output is required. Align governance with SOC 2 Type II, ISO 27001, HIPAA, and CCPA as applicable. Tools like MLflow can provide unified experimentation tracking and governance capabilities.

How do you assess third-party AI vendor risk and supply chain dependencies during due diligence?

Apply a whole-of-value-chain approach covering data suppliers, infrastructure providers, and model vendors. Evaluate whether agents are governed through a centralized control plane.
Explore how Databricks artificial intelligence capabilities can help you build, govern, and secure AI agents across your organization.