How the AI Executive Order shifts vendor management strategies

How the executive order affects vendors

The EO starts from a cybersecurity premise: that advanced AI can help defend systems, find vulnerabilities and support critical infrastructure, but that the same capabilities can create national security risks.

The key vendor management provision is Section 3, "Secure Frontier Model Deployment." It directs the Treasury, the Secretary of War through the National Security Agency, and Homeland Security through CISA, with NIST and other federal offices, to develop a classified benchmarking process for advanced cyber capabilities in AI models. The process determines when a model becomes a covered frontier model -- a risk threshold tied to advanced cyber capability. Because the threshold is classified, enterprises should not expect vendors to disclose the benchmark.

The framework is voluntary. Developers may ask the federal government to assess whether models under development meet the designation and may provide federal access to covered frontier models for up to 30 days before release to other trusted partners, subject to confidentiality, cybersecurity, insider risk, intellectual property, use and nondisclosure protections. This creates an expectation of transparency without a formal approval stamp.

Vendors should be able to state whether they participate, whether a model has been submitted, what government access and confidentiality terms apply and how they manage advanced cyber capability risk. The 30-day window also signals maturity: The vendor can control release candidates, support secure evaluation, protect model weights, respond to findings and manage release discipline.

In parallel, Gold Eagle, the White House's AI cybersecurity vulnerability coordination initiative established in the June EO, points the same way. Vendors are now evaluated on coordinated vulnerability management alongside model quality and feature velocity -- the rate at which new features are brought to users.

Some of the evaluation points include the following:

• Vulnerability intake.
• Customer notification.
• Patch coordination.
• Protection of sensitive vulnerability information.
• Collaboration with agencies, open source maintainers and critical infrastructure operators.

Considerations for AI vendor evaluation

CIOs should not treat the EO as only a federal procurement document. The order mainly directs federal agencies. Participation is voluntary, but standards often shape vendor expectations before they become legal obligations, as SOC 2 did, and frontier AI testing is starting down the same path.

For procurement teams, the EO's "advanced cyber capabilities" should translate into practical due diligence. Some questions to ask include the following:

• Can the model autonomously discover vulnerabilities?
• Can it chain findings across systems?
• Can it generate exploit-relevant code or operational guidance?
• Can it interact with repositories, terminals, ticketing systems, tools or production environments?
• Can it make changes without meaningful human review?
• Can outputs help defenders while also increasing attacker capability?

These answers determine whether a system is made available in a sandbox, development environment, SOC workflow, production network or a more restricted setting.

CIOs should require structured attestations on frontier-risk assessment, voluntary-framework engagement, cybersecurity capability evaluation and procedures to withhold, roll back or constrain a model after evaluation.

Red-teaming should become a default procurement requirement. NIST's AI Risk Management Framework helps identify, measure, manage and govern AI risk. Its generative AI profile, NIST AI 600-1, helps select actions aligned to organizational goals.

Scope should cover the following:

• Prompt injection.
• Insecure output handling.
• Training data poisoning.
• Model denial-of-service.
• Supply chain vulnerabilities.
• Sensitive information disclosure.
• Insecure plugin design.
• Excessive agency.
• Overreliance.
• Unsafe code generation.
• Model theft.

Testing should reflect the actual workflow, including access to email, code repositories, customer records, financial and HR systems, identity stores, APIs and operational technology.

For high-impact uses, such as employment, lending, healthcare, insurance, education or government benefits, vendors should provide documentation supporting compliance with state laws.

For example, Colorado's revised SB26-189, effective January 2027, requires developers of covered automated decision-making technology to give deployers documentation on intended uses, training-data categories, known limitations, appropriate use and human review, and material updates.

California's SB 53 requires large frontier AI developers to publish a framework that incorporates national, international and industry consensus standards, with mechanisms to report critical safety incidents and protect whistleblowers.

Global vendors face overlapping obligations. Under the EU AI Act, general-purpose AI model provider obligations apply since August 2025, European Commission enforcement begins in August 2026, and models on the market must comply by August 2027. So, a single certification may not suffice.

Compliance obligations vary based on who built the model, where it is deployed, what it decides, whether it is general-purpose and whether it sits in a high-impact workflow or has systemic or frontier capabilities.

Intellectual property and confidentiality need separate attention. If a vendor seeks access to customer source code, internal tickets, vulnerability reports, proprietary documents or sensitive prompts, the contract should define the following:

• Ownership.
• Retention.
• Training restrictions.
• Logging.
• Deletion.
• Subcontractor access.
• Audit rights.

Vendors will also protect model weights, prompts, evaluations and system instructions as proprietary, so procurement teams need secure review processes that respect confidentiality without accepting a black box.

Practical vendor due diligence checklist

Diligence is required through all steps of the procurement process, from pre-RFP assessment through the RFP, contract negotiation and ongoing AI vendor management.

The following checklist translates the EO and related governance signals into a due diligence process that can anchor AI procurement strategies.

Pre-RFP assessment

Classify the vendor and AI capability before writing the RFP. A vendor embedding a third-party chatbot in a narrow workflow does not create the same risk as one that develops frontier models, deploys autonomous agents or sells AI-enabled vulnerability discovery.

Key steps at this stage of assessment:

• Determine whether the vendor develops, fine-tunes, hosts, resells or deploys frontier AI models, and whether it is the model developer, application-layer provider, systems integrator, hosting provider or reseller.
• Assess whether the model could plausibly fall within the EO's covered-frontier-model concern and whether the vendor participates in, plans to join or is monitoring the voluntary review framework, keeping participation, nonparticipation and formal approval distinct.
• Review the vendor's history of adversarial testing against the red-team scope described above.
• Review state and international compliance posture against the laws noted above, plus other transparency and sector-specific rules.
• Map the intended enterprise use. Summarization, autonomous remediation, invoice approval, candidate screening and production code generation carry different risk profiles.

RFP requirements

Compliance documentation should go beyond standard security questionnaires. The RFP should require the following structured model and system documentation:

• Model family.
• Deployment model.
• Hosting location.
• Versioning.
• Fine-tuning.
• Tool integrations.
• Retrieval sources.
• Data flows.
• Human review points.
• Fallback modes.

It should also require the following:

• Capability documentation, including supported and prohibited tasks, known limitations, benchmark summaries, safety evaluations and cybersecurity-relevant capabilities.
• Evidence of vulnerability assessments, including internal and third-party testing, remediation history, disclosure policies, patch cadence, dependency management, secure development and model-weight controls.
• Training data transparency sufficient for legal and operational review.
• Customer data protection terms prohibiting use of inputs, outputs, files, logs, prompts, embeddings or derived data for training or product improvement without written consent, plus clear telemetry rules for security and abuse monitoring.
• Update and patch protocols covering testing, notification, version documentation, version pinning and emergency rollback.
• Audit evidence, including SOC 2 Type II, ISO 27001, ISO/IEC 42001, penetration-test and AI safety assessment summaries, vulnerability metrics, subcontractor attestations and alignment to NIST AI RMF or comparable frameworks.
• Explainability appropriate to the use case.

Contract negotiation

Marketing claims and RFP responses should become binding obligations in exhibits, schedules or security addenda.

Key contractual terms include the following:

• Prohibition on customer data use for training, fine-tuning, evaluation, benchmarking or product improvement without prior written consent, covering prompts, outputs, uploaded files, embeddings, logs, metadata, telemetry and derived data, including subcontractor use.
• Incident notice and government reporting obligations when an incident affects the customer's environment, data, operations or compliance.
• AI-specific incident response commitments for prompt injection, unauthorized model actions, output-based data leakage, model-weight or system-prompt compromise, unsafe autonomous behavior and harmful erroneous outputs.
• Audit rights and third-party assessment access for high-risk deployments, plus rollback and version-control procedures.
• Liability allocation for product defects, control failures, unauthorized data use, IP infringement, security failures and missed compliance commitments.
• Change-control obligations for material changes to model capabilities, training practices, hosting architecture, sub-processors, safety filters, logging, retention, cybersecurity controls or government-review status.
• IP and confidentiality protections in both directions, not just for the vendor.

Ongoing vendor management

AI risks evolve as models, integrations, security threats and laws change. A model acceptable in the past may look different in the future after a capability jump, a new tool integration or a legal change. CIOs should set a quarterly review cadence for material AI vendors, covering model updates, new capabilities, incidents, vulnerability findings, red-team results, regulatory and sub-processor changes, customer data use and SLA performance.

Ongoing management should also include the following:

• Monitor participation in voluntary federal processes, Gold Eagle or related coordination efforts, and whether government-facing findings triggered product changes.
• Review new model versions, agentic tools, code-execution features, retrieval connectors and administrative permissions before deployment.
• Maintain updated risk assessments across cybersecurity, data protection, legal compliance, operational resilience, model behavior, IP exposure and business continuity, distinguishing vendor-level from use case-level risk.
• Run periodic red-team or tabletop exercises for important deployments and reassess state and international obligations at least twice a year.

For CIOs, the main point is vendor classification by capability, deployment role and risk pathway. A frontier-model provider, a SaaS vendor with an embedded model, a workflow automation vendor and an AI cybersecurity platform are different kinds of suppliers.

The EO is voluntary, but vendors that document model capabilities, testing practices, data restrictions, update controls and incident obligations will be easier to trust. Vendors that cannot are, in effect, shifting hidden risk to the buyer.

Kashyap Kompella, founder of RPA2AI Research, is an AI industry analyst and advisor to leading companies across the U.S., Europe and the Asia-Pacific region. Kashyap is the co-author of three books, Practical Artificial Intelligence, Artificial Intelligence for Lawyers and AI Governance and Regulation.