AI Agent Due Diligence for Security Leads

A third-party AI agent can read thousands of records, draft summaries, and trigger actions in minutes. It can also expose SharePoint content, create unsafe access paths, or turn a prompt injection into a data-loss event if governance is weak.

This due diligence process gives security leaders a practical way to evaluate the vendor, architecture, permissions, and operating model before sensitive information is connected. I treat the review as a business decision supported by technical evidence, not a generic AI questionnaire.

For Microsoft 365 administrators, the review should begin with the agent’s permissions, data routes, data collection practices, and automation scope.

Key Takeaways

• Begin with a precise inventory of the agent’s permissions, identities, connectors, data stores, outbound services, retention practices, and vendor access.
• Test retrieval grounding, source citations, permission boundaries, prompt-injection resistance, tool restrictions, and human approval for actions that write, send, delete, or change permissions.
• Review Microsoft 365 licensing, Azure consumption, model and token usage, storage, implementation, and support costs before production access, then establish budgets and throttling controls.
• End the assessment with evidence-based deliverables, including a risk register, data-flow map, control gaps, remediation roadmap, executive briefing, and clear go or no-go criteria.
• A formal review may not be worthwhile for public-information tools with no internal data or action-taking capability, or when the business case and workflow ownership are still unclear.

Why AI agent due diligence belongs before deployment

An artificial intelligence agent combines machine learning and natural language processing with tool access, data retrieval, and action-taking capabilities. Unlike a typical SaaS application, it can interpret instructions and trigger actions, broadening the impact of a bad configuration.

These capabilities can support document review, legal due diligence, contract analysis, and financial analysis during mergers and acquisitions. They can also help prepare an investment memo and support internal workflows through workflow automation, retrieving information and triggering approved actions.

A third-party agent might connect to SharePoint, Teams, Exchange Online, OneDrive, Microsoft Graph, Azure AI services, a virtual data room, or a line-of-business platform. Each integration creates a data path and a data collection point that needs review.

For example, an agent handling acquisition analysis may need access to contracts, board materials, financial workbooks, and cybersecurity assessments. Multi-agent systems increase the permissions, handoffs, and failure modes involved in handling those materials. If its retrieval layer ignores existing permissions, a user could receive information they could never access directly.

An agent inherits more than data. It inherits the consequences of every permission, connector, retention setting, and automated action attached to it.

I have found that the strongest AI agent due diligence reviews focus on the difference between what the vendor says the product can do and what the tenant configuration allows it to do. A vendor can have credible security documentation, yet the deployment can still fail because of broad Graph permissions, unmanaged service identities, or data exports that bypass retention controls.

This matters for small business IT teams and venture capital firms, not only large enterprises. Lean teams may rely on agents for sensitive analysis, yet have fewer people to monitor behavior or investigate suspicious prompts. Clear ownership supports risk identification and helps teams unwind incorrect automated actions.

Map permissions, data paths, and vendor access

A useful review begins with a precise data collection inventory. Broad descriptions such as “the agent connects to Microsoft 365” aren’t enough. You need to identify the APIs, identities, scopes, data stores, and outbound services involved.

For each proposed agent, I assess the following evidence:

• The application registration, delegated or application permissions, consent history, service principal owners, and conditional access treatment.
• The Microsoft Graph endpoints, SharePoint sites, Teams channels, Exchange mailboxes, OneDrive locations, Azure subscriptions, virtual data room integrations, and external connectors the agent can reach.
• Data residency locations, encryption, retention, backup practices, subprocessors, support access, incident notification terms, legal holds, and deletion commitments, mapped to regulatory compliance obligations and data privacy requirements.
• Prompt, file, and conversation data collection and logging rules, including whether the vendor stores content for debugging, model evaluation, or service improvement.
• Controls for endpoint security and device hardening when users upload files or access agent outputs from managed workstations.
• Security testing evidence, including penetration-test summaries, vulnerability management procedures, SOC 2 reports when available, and product security architecture documents.

This inventory makes data security and infrastructure optimization practical. A mature cloud infrastructure review identifies where data is duplicated. For instance, an agent may copy SharePoint files into a vendor index, send document chunks to an external model endpoint, and retain chat logs in a separate SaaS tenant. Each copy changes the backup, legal hold, and breach-response picture.

The same discipline applies to specialized systems. Restaurant POS support tools, kitchen technology solutions, data center technology, and operational SaaS platforms may carry employee, payment, supplier, or customer data. A narrow pilot can become an enterprise-wide exposure if connectors expand without approval.

For organizations planning an Office 365 migration, I recommend reviewing AI integrations before moving legacy file shares into SharePoint. Otherwise, old permissions and stale records can become searchable by an agent on day one.

Test retrieval grounding and cross-domain controls

Large language models can generate fluent answers even when their source material is incomplete or irrelevant. That is unacceptable when a document review informs an investment memo for mergers and acquisitions or other investment decisions.

Retrieval-augmented generation, often called RAG, supports a natural language processing workflow. It retrieves approved source material before the model writes an answer. Yet RAG only works when data collection is restricted to approved, permission-aware sources, sources are traceable, and the agent has clear instructions for uncertainty.

A sound test requires citation grounding, with the agent citing the specific source file, location, and retrieved passage behind each material claim. Reviewers should then sample outputs against originals in a second document review, especially for change-of-control clauses, renewal terms, indemnity limits, security obligations, assignment restrictions, and financial covenants.

Multi-agent systems need additional discipline. A legal agent, finance agent, and cybersecurity agent may each produce credible findings. However, the orchestrator agent must preserve source references and clearly separate facts, inferences, and recommendations. It must not combine conflicting results into a confident conclusion.

I also test adversarial documents for prompt injection to make risk identification concrete. A hostile instruction hidden in a document could tell an agent to ignore prior instructions, disclose confidential information, or call an external tool. Strong designs isolate retrieved content from system instructions, restrict tool calls, scan attachments, and require approval for actions that write, send, delete, or change permissions.

This is where cybersecurity services and business continuity & security planning meet AI governance. If the agent or its dependency fails, your team needs a documented manual process, audit trail, rollback plan, and an owner who can disable access quickly.

Review Microsoft 365 licensing and consumption costs

Microsoft 365 Copilot, Copilot Studio, Azure-hosted models, and third-party agents use different commercial models. Treating them as one line item creates budget surprises and weak accountability.

Microsoft’s Copilot Studio billing and licensing guidance explains that agent usage can involve consumption-based billing. Microsoft’s extensibility cost considerations also distinguish between licensed users and usage-based options in some configurations.

If a vendor proposes $99/user/month, treat that amount as licensing only. Azure compute, machine learning model and token consumption, storage, message consumption, implementation work, and support may be billed separately unless the contract explicitly includes them.

For Microsoft-native projects, validate entitlement against Microsoft 365 Copilot plans and pricing, then map each agent workload to the correct billing account. I recommend setting Azure budgets, tags, alerts, and monthly consumption reviews before production access begins.

Cloud management should also include a throttle policy. Set transaction limits, cap expensive model calls, limit retention windows, and require approval before a new connector accesses high-value content. A secure cloud architecture is also a cost-control design because uncontrolled workloads can create financial and security exposure.

Deliverables that support a go or no-go decision

A worthwhile third-party agent review ends with evidence your leadership team can use. A formal risk assessment should clarify risk identification, ownership, and remediation priorities. Technical owners should not have to guess from a vendor scorecard.

My recommended deliverables include:

• A risk register that ranks findings by business impact, likelihood, affected data, and accountable owner.
• A data-flow map for multi-agent systems showing each agent handoff, retrieval source, model request, output, and tool call. It should also show where prompts, retrieved documents, and logs travel.
• Control-gap findings tied to actual configurations and regulatory compliance obligations. Examples include excessive Graph permissions, missing MFA requirements, absent logging, or unapproved data retention.
• A prioritized remediation roadmap that separates launch blockers from post-launch improvements.
• An executive briefing that explains the commercial exposure, operational benefits, remaining risks, and decision points in plain language. Its findings should inform an investment memo when the deployment supports a transaction or investment committee review.
• Go or no-go criteria, including compliance checks and minimum controls for identity, data isolation, human approval, monitoring, contract protections, and incident response.

This approach supports tailored technology services instead of a generic security review. It also fits a broader IT strategy for SMBs, where a business technology partner may be managing cloud modernization, digital transformation, managed IT for small business, and technology consulting at the same time.

When the review is not worth the investment

A formal engagement may not be necessary when an agent has no access to internal data, cannot take actions, and uses only public information. A brief security intake may be enough for a low-risk research tool.

The same applies when the business case is unclear. If no team owns the workflow, no measurable problem exists, and users have not identified the required data, a detailed review will only document an immature idea. Start with process design first.

Delay the investment if your Microsoft 365 tenant still has unmanaged privileged accounts, inconsistent MFA enforcement, uncontrolled file sharing, or weak endpoint security. Strengthening these foundations usually creates more value than deploying another tool.

Frequently Asked Questions

What should security leaders review first when evaluating an AI agent?

Start with the agent’s permissions, identities, connectors, data routes, collection practices, and automation scope. Confirm which APIs, repositories, external services, and vendor personnel can access the information.

How should an AI agent’s retrieval and security controls be tested?

Require citations that identify the source file, location, and relevant passage behind each material claim, then compare outputs with the original documents. Test adversarial documents for prompt injection and verify that tool calls, sensitive actions, and cross-domain access require appropriate restrictions or approval.

What costs should be included in an AI agent due diligence review?

Do not evaluate only the per-user license price. Include Azure compute, model and token consumption, storage, message usage, implementation, support, retention, and any third-party services, then establish budgets, alerts, and workload limits.

What deliverables support an AI agent go or no-go decision?

The review should produce a risk register, data-flow map, configuration-based control gaps, remediation roadmap, executive briefing, and minimum launch criteria. These deliverables give leadership a clear view of business value, commercial exposure, remaining risk, and accountable owners.

When is a formal AI agent review unnecessary?

A brief security intake may be sufficient when the agent uses only public information, has no access to internal data, and cannot take actions. Delay a detailed review when the workflow has no owner or measurable business case, or when foundational Microsoft 365 controls such as MFA, privileged access, file sharing, or endpoint security remain weak.

Make the decision with evidence

AI agents can streamline repetitive document review and improve access to institutional knowledge. They can also amplify weak permissions and unclear data handling.

A disciplined AI agent due diligence review gives you a defensible basis to approve, limit, redesign, or decline a vendor deployment. The best outcome is not the fastest launch. It is an agent that fits your security controls, budget model, and operating capacity.

If you’re considering a third-party agent, schedule a readiness assessment or Microsoft 365 licensing review before granting production access.