In 2023, when an AI product had a security vulnerability, it was an engineering problem. The security team was alerted, an engineer fixed it, and the business continued. The vulnerability rarely surfaced in commercial conversations.
That changed in 2025. Enterprise buyers — particularly in financial services, healthcare, and professional services — began including AI-specific security requirements in vendor questionnaires. By 2026, a documented security architecture for your AI system is a prerequisite for enterprise procurement, not a differentiator. Products that cannot demonstrate it are disqualified before the commercial conversation begins.
of enterprise AI vendor assessments in 2026 include specific questions about AI-unique security risks — attacks that did not exist in traditional software procurement
The Security Risk That Enterprise Buyers Are Most Concerned About
The primary AI-specific security risk that enterprise procurement teams are assessing is the ability for external content — emails, documents, web pages that your AI processes — to contain hidden instructions that manipulate your AI system into taking unauthorised actions.
In plain terms: if your AI product can read a document and that document contains a hidden instruction telling the AI to 'export all customer data to this external address,' can your system prevent the AI from following that instruction? This is not a hypothetical. It is a documented, real-world attack vector that has been demonstrated against deployed AI systems, including customer-facing products.
Enterprise buyers are asking this question because the consequences of a successful attack on an AI system with access to their data are significantly more severe than a traditional data breach. A traditional breach typically requires an attacker to exploit a software vulnerability. An AI-specific attack can be executed by anyone who can get content into the data your AI processes — which, for an AI product that reads emails or documents, is potentially anyone.
The Commercial Impact of This Risk
We are currently working with several AI product companies who are rebuilding their security architecture after losing enterprise deals to this specific gap. The pattern is consistent: the product is technically excellent, the commercial team has built strong relationships, but the procurement questionnaire reveals that the AI system has no documented protection against this class of attack. The deal stalls or dies.
The cost is not just the lost deal. It is the three to six months of engineering work required to implement proper security controls retrospectively, plus the cost of rebuilding the pipeline with those accounts.
The Three Security Controls Enterprise Buyers Require
- —Minimal access permissions: Your AI system should only have access to the tools, data, and external systems it needs for the specific task it is performing. An AI that reads documents should not be able to send emails. An AI that answers customer questions should not have access to internal financial records. The smaller the blast radius of a potential attack, the less concerning it is to a buyer.
- —Human approval for consequential actions: Any AI action that is irreversible or communicates externally — sending an email, modifying a record, calling an external API — should require explicit human approval before execution. This is both a security control and a feature your enterprise buyers consider a sign of product maturity.
- —Complete audit logging: A full, tamper-proof log of every action your AI system takes, what data it accessed, and what output it produced. This is what your customer's security team will ask to see if they ever investigate an incident — or if they are doing due diligence before signing a contract.
The companies winning enterprise AI deals are not necessarily the ones with the most technically impressive AI. They are the ones who can hand a security questionnaire to their VP of Engineering and have it filled out completely and specifically within 48 hours.
Building Security In vs. Bolting It On
Like most engineering decisions, getting security right at the design stage costs a fraction of what it costs to retrofit it later. We have run both types of engagements — greenfield security architecture and retrospective security hardening — and the cost difference is significant. A security architecture designed from the start for an AI product typically adds two to four weeks to a development timeline. Retrofitting it to an existing product typically costs two to four months.
If you are building an AI product and enterprise is a target market, the right time to address this is before your first enterprise pilot — not during it.