The era of accepting an artificial intelligence vendor’s assurances on data security and ethical practices at face value has decisively come to an end, fundamentally reshaping the procurement landscape for enterprise technology. As AI systems become deeply embedded in core business operations, the concept of “trustworthy AI” has evolved from an abstract marketing term into a rigorously defined and continuously monitored standard. Chief Information Officers are now leading a charge for verifiable proof of responsibility, armed with stringent governance frameworks and a readiness to walk away from any partnership that fails to provide concrete, contractual evidence of its commitment to transparency and security. This shift marks a new chapter where trust is not granted but meticulously earned and perpetually validated.
The New Standard From Abstract Promises to Concrete Proof
The End of the Leap of Faith Era
The maturation of the artificial intelligence ecosystem has forced a critical transition where trust must be earned through perpetual validation rather than assumed. In the past, enterprises may have accepted vendor assurances regarding responsible data handling, but that dynamic has been replaced by a new paradigm where formal frameworks for trustworthy AI are no longer a competitive differentiator but have become, as 6sense CIO Bryan Wise predicts, “table stakes.” This evolution is driven by the intrinsic nature of modern AI, which frequently requires the ingestion of immense volumes of sensitive corporate information, including valuable intellectual property and personally identifiable information (PII). Consequently, CIOs and Chief Information Security Officers (CISOs) are now intensely focused on several core tenets of trust: demanding absolute certainty that enterprise data is secure, that it is used exclusively for the explicitly stated purposes, and that the data being collected is genuinely necessary to achieve the promised business outcomes.
This heightened scrutiny reflects a fundamental understanding that the risks associated with AI are not merely technical but existential to the business. The potential for data misuse, security breaches, or compliance failures carries significant financial and reputational consequences, making the vendor selection process a high-stakes endeavor. As a result, technology leaders are establishing non-negotiable principles for their AI partners, requiring them to provide transparent and verifiable answers about their data governance practices. Any vendor that cannot offer clear architectural diagrams illustrating data flows, detailed policies on data retention, and robust security protocols is increasingly viewed as an unacceptable liability. This detailed due diligence, once reserved for the most critical systems, is now a standard prerequisite for any new AI tool adoption, ensuring that technological innovation does not come at the expense of enterprise security and integrity.
Navigating a Dynamic Threat Landscape
Establishing and maintaining trust is further complicated by the breakneck pace of AI development, which renders static, one-time evaluations dangerously obsolete. Appfire CISO Doug Kersten notes that because the technology is evolving so rapidly, achieving “full trust” is an exceedingly difficult moving target. The sentiment is echoed by NIST principal researcher Martin Stanley, who cautions against the futile desire for a “set-and-forget” solution to trustworthiness. Unlike traditional software, AI systems are not static; they permeate an organization, learn from new data, and change their behavior, making a one-time compliance check insufficient. This reality necessitates a fundamental shift in mindset, moving away from a checklist-based approach toward a model of continuous, dynamic risk management that adapts alongside the technology itself.
To navigate this complexity, organizations are increasingly relying on established standards and certifications as a foundational baseline for vendor evaluation, treating them not as the end of the inquiry but as the beginning. Frameworks such as the NIST AI Risk Management Framework, alongside proven compliance certifications like ISO 27001 and SOC 2 Type 2, are becoming prerequisites for even being considered as a potential partner. For instance, Kersten’s firm mandates a SOC 2 or ISO audit for all its mission-critical vendors, refusing to engage with those who cannot provide this essential external validation. This reliance on recognized standards provides an objective starting point for due diligence, allowing security and IT leaders to focus their deeper, more qualitative assessments on vendors that have already demonstrated a baseline commitment to security and operational maturity.
Institutionalizing Scrutiny The CIOs Playbook for AI Vetting
Building a Governance Framework
A significant trend emerging to manage the complexities of AI adoption is the formalization of vendor evaluation within corporate governance structures. The decision to onboard a new AI tool is no longer made in a departmental silo but has become a collaborative, cross-functional effort. Enterprises are establishing dedicated governance committees comprising leaders from security, IT, legal, procurement, and relevant business units to work in concert. These committees are tasked with defining the organization’s official stance on AI, asking critical questions such as, “How do we define an AI vendor? What use cases will we allow or disallow? What steps must be taken to permit employees to use AI tools without introducing unacceptable security, privacy, or data loss risks?” This holistic approach ensures that any vendor selection aligns with the company’s comprehensive risk posture and overarching strategic objectives.
Further institutionalizing this oversight, some companies are creating a dedicated “AI czar” role, a position noted by Aimee Cardwell, CIO and CISO in residence at Transcend. This individual acts as a central point of contact responsible for evaluating AI use cases across the enterprise, assessing their business value against potential risks, and ensuring a consistent standard of inquiry is applied to all vendors. Regardless of the specific organizational model, the scrutiny process extends far beyond simply checking for a certification. While frameworks like the NIST AI Risk Management Framework provide an excellent starting point, savvy leaders understand its voluntary nature. As Kersten points out, the crucial follow-up question is not whether a vendor uses the framework, but “How deeply do you align to it?” This signals a decisive move toward a more qualitative, in-depth due diligence process designed to uncover a vendor’s true commitment to responsible AI.
Asking the Tough Questions
To truly gauge a vendor’s trustworthiness, CIOs and CISOs are arriving at negotiations armed with a specific and challenging set of questions designed to probe deep into a vendor’s technology, processes, and philosophy. The overarching consensus is that these tough conversations must happen long before any contract is signed, setting a new standard for pre-procurement diligence. A paramount concern is data use and protection, where leaders demand absolute clarity on what data is being used, where it is being sent, and how it is being secured. Cardwell suggests challenging the very premise that a vendor needs raw customer data, advocating instead for the use of tokenized or de-identified data to achieve similar results with far less risk. Enterprise buyers now increasingly demand detailed architectural diagrams illustrating the complete data flow through a vendor’s system, leaving no room for ambiguity.
The inquiry does not stop at primary data. Scrutiny now extends to the exploitation of metadata, a subtle but critical new frontier in vendor vetting. Kersten highlights that some vendors may claim to protect customer data while simultaneously using their metadata for predictive analytics and other purposes, potentially exposing a company to privacy and competitive risks. Furthermore, a vendor’s ability to handle data deletion requests serves as a powerful litmus test for its data governance maturity. Asking a vendor to detail its process for fulfilling a “right to be forgotten” request, as Cardwell explains, can quickly reveal the true extent of its control over the data it processes. A confident, clear answer indicates a well-architected system, whereas hesitation suggests potential data management deficiencies that could become significant liabilities down the line.
Probing Deeper Into Vendor Integrity
Beyond technical protocols, the investigation has expanded to cover a vendor’s origins and long-term viability, as these factors are increasingly seen as indicators of trustworthiness. Kim Huffman, CIO at Workiva, emphasizes the importance of understanding how a vendor’s AI capabilities were developed. Was the AI built from the ground up and deeply integrated into the core product, or was it a “bolt-on” capability acquired from another company? Acquired technologies may not share the same data model or security posture as the core system, potentially creating vulnerabilities and integration challenges. Uncovering this early can prevent significant downstream problems and ensure that the advertised capabilities are not merely a superficial addition to an existing platform. This line of questioning helps determine if a vendor’s commitment to AI is strategic and foundational or merely opportunistic.
Especially in a market saturated with startups, the people and funding behind the technology matter immensely. Bryan Wise advises a thorough examination of a vendor’s leadership team and financial backing as part of the due diligence process. Key questions include: Who are the founders, and do they possess deep domain expertise relevant to the problem they are solving? Who are the investors, and is the company well-funded enough to ensure its long-term stability and support? Can the leadership team articulate a clear, confident vision and a realistic roadmap for the future? A vendor with a solid financial foundation and experienced leadership is more likely to be a reliable long-term partner. This comprehensive evaluation culminated in legally binding agreements, as the contract served as the ultimate safeguard, ensuring partners were not just technologically capable but also trustworthy stewards of an organization’s most valuable assets.
