What Do Investors Expect Us to Have in Place for AI Security?

Investors assess AI security when AI sits in the product or in core operations. Diligence concentrates on model provenance, data rights, dependence on third-party model suppliers and governance, with depth scaling by stage. Seed processes test whether founders answer factually. Growth processes ask for documented evidence.

What do investors expect us to have in place for AI security?

An accurate account of every AI system in the business: what data feeds it, where the model came from and which executive is accountable for it. Alongside that account, investors look for controls proportionate to the risk, principally access restrictions on training and inference data and contractual clarity over data rights.

The bar moves with stage. A seed-stage company is not expected to hold ISO 42001 certification. It is expected to answer factual questions without guessing, and an AI Security Gap Analysis produces the control map most processes assume already exists.

What does AI due diligence cover in a fundraising process?

Four blocks, in most processes. Model provenance covers which models you use, whether they are self-hosted, fine-tuned or called through an API and what licence terms apply. Data rights cover whether you hold a legal basis to train on or process the data you use. Dependence covers how much product value rests on a single third-party model supplier and what happens if pricing, terms or availability change.

Governance is the fourth block: who decides what an AI system is allowed to do and how that decision is recorded. Regulatory exposure runs across all four, and where you sell into the EU an investor will want your position under the EU AI Act, particularly whether any system falls into a higher-risk classification.

How do seed-stage and growth-stage expectations differ?

At seed, diligence is largely factual and forward-looking. Investors want an accurate inventory, honest disclosure of what is unfinished and a credible view of what you will build as you scale, which a two-page description of your AI stack, data flows and model licence terms often satisfies. What damages a seed process is contradiction between founder answers and technical reality.

At Series A and beyond, diligence becomes evidential: policies, access logs, model and vendor contracts, DPIAs where applicable and a single accountable executive. Organisations needing continuity rather than a one-off exercise typically move into an ongoing AI Security Programme.

What should be in the data room before a process opens?

Five artefacts cover most of what is requested. Assemble them before the process opens rather than during it, because producing material under time pressure invites errors. Inconsistency between documents attracts far more scrutiny than an acknowledged gap carrying a date.

• An AI system inventory listing each system, its purpose, the model behind it and its owner.
• A data flow description showing what enters each system, where it is processed and where it is retained.
• Model and vendor contracts with the licence and data-rights position marked up.
• An AI use policy setting out what staff and systems may and may not do.
• A risk register recording known issues with remediation dates.

Where a gap is structural rather than documentary, record it with a date rather than leaving an investor to find it.

What do investors ask about AI governance specifically?

Governance questions probe decision rights rather than technology. Who approves a new AI system entering production? Who answers if a model produces a harmful or incorrect output that reaches a customer? Is the pre-launch review of a model documented, and how do you know which AI tools your staff are using?

That last question exposes Shadow AI, and founders answer it worst. Unsanctioned tool use is common in fast-growing companies and investors know it, so a founder who runs quarterly discovery and reports the findings reads as better controlled than one who claims none exists. Our answer on who should be responsible for AI covers how that accountability is written down.

How does AI risk affect valuation and deal terms?

Findings rarely stop a raise. They reprice it. An unclear legal basis for data used in training is the most common route from a technical finding to a commercial one, because it creates a contingent liability an investor will either discount for or paper over with a warranty and an indemnity.

Dependence on a single model supplier has a similar effect, since concentration risk in a core input reads as a margin question rather than an engineering one. A structured AI risk assessment lets you quantify both before someone else does.

How is AI risk assessed differently from conventional security risk?

Conventional security risk centres on confidentiality, integrity and availability of systems and data. AI adds further dimensions: whether a model behaves as intended, whether its outputs can be trusted, what authority it holds to act and whether its decisions can be explained to a customer or a regulator.

An investor asking about AI risk is usually asking two questions at once: is the system secure, and does it do what you claim. Penetration test results address the first. Evidence from AI Behaviour Verification addresses the second, and founders who present only conventional security evidence find the AI-specific questions returning later in the process.

What happens if we fail an AI diligence review?

Outright failure is rare. The common outcomes are a slower close, tighter warranties and a price adjustment where a specific liability is identified. Management credibility takes the heaviest damage, because an investor who catches a material AI system the founders did not disclose will reread every other answer.

Where findings are structural rather than documentary, expect a condition to close with a remediation deadline attached.

When should we do this work relative to the raise?

Before the process opens, and ideally twelve months ahead of it if AI is central to the business. Remediation before a raise is cheaper than remediation as a condition of one, and it happens on your engineering schedule rather than the investor’s timetable with terms already anchored.

If the next raise is nearer than that, prioritise the two findings that move terms: data rights and model supplier concentration. For related reading, see our AI Security Projects work, our note on ISO 42001 certification cost and the AI governance entry. This page is general information and does not constitute legal, regulatory or investment advice.