The 10-Point AI Partner Evaluation Checklist: How to Spot AI-Washing Before You Sign
Your channel partner's AI capability constrains your organization's AI potential. Most enterprises access AI through resellers and VARs — which means a partner who has added AI to their marketing without building genuine competency becomes your organization's ceiling. This 10-point scorecard, drawn directly from Chapter 11 of The AI Strategy Blueprint, gives you the evidence-based criteria to separate real AI practices from brand new AI logos on a decade-old slide deck.
What Makes a Great AI Channel Partner?
A genuine AI channel partner demonstrates four observable characteristics before they ever quote you: proactive investment in AI ahead of demand, systematic customer engagement processes, security-first positioning for regulated industries, and services development that sustains a self-funding AI practice. Partners who lack these markers have added AI to their marketing without building the competency to deliver. The 10-point checklist below operationalizes these criteria into a go/no-go scoring grid you can complete in a single partner meeting. The vTECH io case study — $5–6M in Year 1 net new AI revenue from 1,300 customers across five states — provides the benchmark against which all other partners should be measured. See also: The AI Partner Blueprint companion book and our AI-washing detection guide.
Jump to the Scorecard- What Is an AI Channel Partner?
- Why 84% of Organizations Use 2+ Vendors
- The 4 Characteristics of Excellent AI Partners
- The 10-Point AI Partner Evaluation Scorecard
- The vTECH io Case Study
- The ISV Partnership Evaluation Matrix
- The AI Partner Blueprint Companion Book
- Tiered Partner Prospect List
- Frequently Asked Questions
What Is an AI Channel Partner?
According to a BCG analysis of enterprise AI sourcing patterns, the vast majority of organizations do not purchase AI solutions directly from the software vendors who create them. They work through channel partners, value-added resellers (VARs), and systems integrators who bundle technology with implementation, training, and ongoing support.
This intermediary layer exists because technology alone does not produce business outcomes. AI solutions require configuration, integration into existing workflows, organizational change management, and sustained support to deliver value. A channel partner serves as the execution layer between an AI vendor's capability and your organization's realized results.
“Your channel partner’s AI capability constrains your organization’s AI potential. You cannot access AI excellence through a partner who has not built AI excellence themselves.”
— John Byron Hanby IV, The AI Strategy Blueprint, Chapter 11
The AI era transforms the channel partner relationship fundamentally. When purchasing traditional software, a partner's primary value lay in procurement efficiency, technical installation, and basic support. AI solutions demand far more: understanding of AI technology categories, sophisticated ISV evaluation, use case prioritization guidance, and adoption support that extends well beyond technical deployment. A partner equipped to sell servers and configure networks may lack the capability to guide an AI transformation.
This distinction matters because the wrong partner selection compounds. Partners who have added AI to their marketing without building genuine competency consume your resources while delivering little. Partners who have built mature AI practices accelerate time-to-value and transfer knowledge that builds internal capability. The partner evaluation frameworks in this article — sourced from Chapter 11 of The AI Strategy Blueprint — ensure you distinguish between these outcomes before you commit.
The partner you choose for AI initiatives shapes your organization's AI trajectory for years. Partners with mature AI practices transfer knowledge that builds internal capability. Partners who have added AI terminology to existing offerings leave you dependent on a partner who cannot answer the next question. Choose accordingly.
Before evaluating specific partners, it helps to understand the competitive landscape they operate within. For a full treatment of how to evaluate AI vendors (as distinct from partners), see our companion piece: AI Vendor Evaluation Checklist: 12 Questions Before You Sign. For the governance framework that determines which AI solutions your partners can even propose, see Building an AI Governance Framework.
Why 84% of Organizations Use 2+ Vendors on AI Initiatives
According to BCG research on AI vendor collaboration, eighty-four percent of organizations work with two or more vendors on AI initiatives. This multi-vendor reality is not a sign of dysfunction — it reflects the genuine breadth of AI capability required for enterprise transformation. No single vendor provides best-in-class solutions across infrastructure, model hosting, data security, workflow integration, and workforce training simultaneously.
“Eighty-four percent of organizations work with two or more vendors on AI initiatives, and this multi-vendor reality extends to the partner layer as well. When multiple partners contribute to AI initiatives, clear role definition becomes essential.”
— BCG, How to Collaborate with Vendors to Maximize GenAI Success, 2025
The practical implication: your AI partner selection is rarely a single decision. You will work with multiple partners across different capability domains — and the quality of each selection contributes to or constrains your overall AI trajectory. The evaluation rigor you apply to your primary channel partner should extend to every partner who touches your AI stack.
Multi-vendor environments also amplify the cost of a poor partner selection. When multiple partners contribute to AI initiatives, clear role definition becomes essential. Which partner owns which capability? How do different partners' contributions integrate? Who provides support when issues cross partner boundaries? Organizations that answer these questions proactively avoid the finger-pointing that accompanies multi-partner environments.
For context on how your channel partners fit within the broader AI ecosystem including cloud providers, ISVs, and systems integrators, see our pillar guide: Enterprise AI Strategy: The Complete Framework. For the specific challenges of managing AI at scale across multi-vendor architectures, see Hybrid AI Architecture Decision Guide.
The 4 Characteristics of Excellent AI Partners
Abstract evaluation criteria become meaningful when grounded in concrete example. vTECH io, a technology solutions provider serving customers since 1997, demonstrates what AI partnership excellence looks like in practice. The four characteristics that drove their $5–6M Year 1 result provide the diagnostic framework for evaluating every potential partner you consider.
Proactive Investment
Genuine AI partners allocate R&D budget to AI initiatives before customer demand materializes. vTECH io negotiated marketing development funds from vendor partners, purchased bulk licensing ahead of customer demand, and invested in training their team before the first customer conversation. Partners who wait for customer requests before investing in AI capability will always lag behind requirements.
Systematic Customer Engagement
Excellent partners have documented AI engagement processes, not ad hoc pitches. vTECH io implemented a specific protocol: two weeks after every PC delivery, they contact customers to introduce bundled AI capability and schedule demonstrations. This systematic approach transforms every hardware transaction into an AI opportunity. Partners with ad hoc approaches miss these compounding conversion moments.
Security-First Positioning
Partners serving regulated industries — healthcare, government, financial services — must lead with data security, not treat it as a compliance afterthought. vTECH io recognized that their SLED and healthcare customers (comprising 50–60% of their base) required AI that eliminates data transmission risks entirely. They partnered with Iternal Technologies to offer AirgapAI, which operates entirely within local environments with no cloud data exposure. This security posture enabled conversations that cloud-dependent AI solutions cannot address.
Services Development
Partners who offer products without corresponding services leave value on the table and limit their ability to support customers through the full AI adoption journey. vTECH io developed consulting capabilities for customers ready to expand beyond initial deployments. Within eleven months, their consulting revenue covered all bundling costs, creating a self-sustaining AI practice. This is the economics of a real AI practice, not a reseller with an AI badge.
“Partners who have built genuine AI practices can speak to revenue generated, customer outcomes achieved, and expansion patterns observed. Partners who cannot provide such evidence may have added AI to their offerings without building the underlying capability that produces measurable results.”
— John Byron Hanby IV, The AI Strategy Blueprint, Chapter 11
The 10-Point AI Partner Evaluation Scorecard
Before committing to a channel partner for AI initiatives, verify capability across the ten dimensions below. This scorecard synthesizes the evaluation criteria from Chapter 11 of The AI Strategy Blueprint into a single verification framework. Each criterion represents an investment that credible partners have made; gaps in any dimension signal areas requiring deeper investigation or alternative partner consideration.
Score each criterion: 2 = Strong evidence, 1 = Partial evidence, 0 = No evidence / deflection. Partners scoring 16–20 merit deep engagement. Partners scoring below 10 should be deprioritized regardless of relationship history or capabilities in non-AI domains.
| # | Criterion | What Good Looks Like | Red Flags | Score (0–2) |
|---|---|---|---|---|
| 1 |
Documented AI Strategy Written strategy with investment timeline, not a slide deck assembled for your meeting |
Partner presents a capabilities deck with a dated AI strategy, team composition, ISV partnerships, and methodology. The deck is consistent across every sales conversation. | Generic AI slides without organizational specificity. Partner assures you they are "all-in on AI" without documentation. | |
| 2 |
Dedicated AI Practice Team Named personnel with AI-specific roles, not generalists assigned to cover AI requests |
Partner provides an org chart showing AI team composition. AI specialists have tenures measured in years, not months. They have hired or developed people before your inquiry arrived. | AI knowledge concentrated in one individual. Team assembled recently in response to market demand. No org chart available for the AI practice. | |
| 3 |
Platform Certifications Current, verifiable certifications from the AI platform vendors they represent |
Partner provides certification documentation with names, dates, and specializations. Certifications are current (within 12–18 months) and relevant to the solutions they propose for you. | Partner references "partnerships" without certifications. Certifications are expired or belong to personnel who have left. Cannot name specific certified individuals. | |
| 4 |
ISV Tier Standing Meaningful tier level with AI ISVs, not a basic sign-up agreement |
Partner can articulate what tier they hold with each AI ISV they represent, what requirements they met to achieve it, and how they maintain standing. Premium or elite tiers indicate validated competency. | Partner holds only basic or entry-level tier status. Cannot explain tier requirements. Represents many AI ISVs superficially rather than a few deeply. | |
| 5 |
Implementation Track Record Completed AI implementations in industries comparable to yours |
Partner can describe specific completed AI implementations: industry, use case, timeline, challenges encountered, and outcomes achieved. They have done this in your vertical or a comparable regulated environment. | Partner can only describe implementations at a generic level. All examples involve basic configurations requiring minimal partner value-add. No industry-specific examples relevant to your requirements. | |
| 6 |
Customer References References who will describe AI implementations in detail, not just general satisfaction |
Partner provides contact information for references who have completed AI deployments. References describe specific outcomes, timelines, and challenges. References are in comparable industries and organization sizes. | Partner deflects to general customer satisfaction references. AI-specific references require weeks to arrange. References describe only basic configurations or initial pilots that never expanded. | |
| 7 |
Delivery Methodology A repeatable AI delivery methodology with documentation from prior engagements |
Partner walks you through a specific AI delivery methodology covering assessment, pilot design, deployment, and adoption support. The methodology includes real case examples and reflects accumulated experience across multiple engagements. | Partner describes only vendor implementation guides. Methodology conversation devolves to generic project management frameworks. No AI-specific methodology documentation exists. | |
| 8 |
Security and Compliance Posture Documented security practices matching your requirements, plus ISV compliance documentation |
Partner can document the security architecture of every AI solution they propose, including data residency, transmission policies, and compliance certifications. For regulated industries: HIPAA, FedRAMP, CMMC, ITAR documentation is ready to share. | Security conversation is deferred to the ISV. Partner cannot explain how proposed solutions handle your most sensitive data. Compliance documentation requires weeks to assemble. Cloud-first defaults without on-premises or air-gap alternatives. | |
| 9 |
Change Management and Adoption Support Training curricula and adoption support materials beyond technical deployment |
Partner offers structured training programs for end users, not just admin configuration guides. They have adoption playbooks, curricula, and success metrics from prior deployments. They understand that the 70% of AI success that is people-dependent is their responsibility too. | Partner's support ends at go-live. Training is limited to vendor documentation. No post-deployment adoption metrics from prior engagements. Change management is treated as the customer's problem. | |
| 10 |
Internal AI Adoption Partner uses AI in their own operations before selling it to customers |
Partner describes specific ways they use AI internally: sales process automation, proposal generation, customer research, support ticket handling. They have lived the adoption challenges they will help you navigate. They use AI themselves before selling it. | Partner has not deployed AI internally. Cannot speak credibly to implementation challenges from direct experience. Their AI knowledge is theoretical, drawn entirely from vendor materials rather than operational experience. | |
| Total Score — 16–20: Proceed | 10–15: Probe Gaps | 0–9: Deprioritize | ||||
This scorecard is designed to be completed in a single partner capabilities meeting. Request a capabilities presentation and evaluate responses against each criterion in real time. Partners who have built genuine AI practices answer these questions fluently because they have lived the investment. Partners engaged in AI-washing deflect, generalize, and defer.
The AI Strategy Blueprint
Chapter 11 of The AI Strategy Blueprint contains the complete partner evaluation framework — including the ISV evaluation matrix, the vTECH io case study in full, and the methodology for sharing the companion AI Partner Blueprint with your existing IT partners to accelerate their AI capability development. The book is available on Amazon for $24.95.
The vTECH io Case Study: What Partner Excellence Produces
vTECH io, a technology solutions provider that has served customers since 1997, provides the clearest available benchmark for what genuine AI partnership looks like in practice. Their results from the first year of their deliberate AI strategy define the upper bound of what is achievable — and the baseline against which you should evaluate every partner who claims AI excellence.
vTECH io serves approximately 1,300 customers across Florida, Georgia, Ohio, Texas, and Alabama. Their customer base spans SLED (State/Local Government and Education) at 35–40%, healthcare at 15–20%, with significant presence in finance, legal, and other verticals. This vertical diversity matters: it demonstrates a partner's ability to navigate different regulatory environments, security requirements, and use case profiles simultaneously.
“Every PC you get from us is AI ready. That’s our message. That’s our marketing. That’s our go-to-market strategy.”
— Chris McDaniel, CRO, vTECH io
Under the leadership of Chris McDaniel, Chief Revenue Officer, vTECH io developed a deliberate AI strategy. Their approach illustrates all four characteristics of excellent AI partners simultaneously. They invested proactively, purchasing bulk licensing ahead of demand and negotiating marketing development funds. They engaged systematically, implementing a two-week post-PC-delivery follow-up protocol that consistently introduced AI capability to hardware customers. They positioned security-first, partnering with Iternal Technologies to offer AirgapAI — a solution that operates entirely on-device with no cloud data exposure — specifically to address the SLED and healthcare customer requirements that dominate their base. And they developed services, building a consulting practice whose revenue covered all bundling costs within eleven months.
McDaniel emphasized a principle that the checklist's tenth criterion captures directly: effective AI partners use AI themselves before selling it. vTECH io invested heavily in internal training, creating what they describe as a learning hub within their organization. This internal adoption gave their team direct experience with the implementation challenges, adoption barriers, and value realization patterns that their customers would encounter — credibility that cannot be manufactured from vendor training materials alone.
When evaluating any potential AI partner, ask them to describe their equivalent of the vTECH io results: What AI revenue have they generated? What customer expansion patterns have they observed? How long did it take their AI practice to become self-sustaining? Partners who have built genuine practices speak to these metrics fluently. Partners who have not will change the subject.
McDaniel also characterized the AI sales process as farming: planting seeds, tending carefully, and waiting for harvest. Partners who treat AI as a quick-win sales opportunity may abandon customers when results take time to materialize. Partners who understand the patient nature of AI adoption maintain commitment through the extended timelines that enterprise AI transformation requires. This patience is itself a differentiator — and one worth probing explicitly during partner evaluation conversations.
The ISV Partnership Evaluation Matrix
A channel partner's AI capability is substantially determined by the ISV partnerships they have cultivated. The software vendors a partner can offer, and the depth of those relationships, constrain what AI capability you can access through that partner. Partners who have invested in relationships with leading AI ISVs have made commitments that require ongoing investment: training staff, maintaining certification currency, developing implementation expertise, and dedicating resources to the partnership.
The following matrix, drawn from Chapter 11 of The AI Strategy Blueprint, provides a structured approach for assessing channel partner ISV relationships. Apply this matrix to every AI ISV a partner claims to represent.
| Evaluation Dimension | Questions to Ask | Why It Matters |
|---|---|---|
| Partnership Tier | What tier have you achieved with this ISV? What were the requirements to achieve it? How long have you held this tier? | Higher tiers require demonstrated competency and investment. A partner who cannot articulate tier requirements likely achieved only basic registration. |
| Certified Personnel | How many certified professionals do you have on this platform? In what specializations? Are certifications current? | Certifications validate genuine expertise on the platform. Ask for names and dates; verify against ISV partner directories where available. |
| Implementation Count | How many implementations have you completed? In what industries? What was the range of complexity? | Experience predicts capability and reveals vertical expertise. Volume matters less than industry relevance to your requirements. |
| Reference Availability | Can you provide references who have completed implementations of this ISV’s solutions in environments comparable to ours? | References validate claims and reveal customer satisfaction. The speed with which a partner produces references is itself a signal. |
| Joint Go-to-Market | Does the ISV actively co-sell with you? Do they refer opportunities to your organization? | ISV referrals indicate trust in partner capability. Partners the ISV actively co-sells with have demonstrated results worth the ISV's endorsement. |
vTECH io's ISV selection rationale illustrates how this matrix works in practice. Their choice of Iternal Technologies as primary AI ISV reflected four specific requirements: security posture (AirgapAI's on-device architecture for healthcare and government), deployment simplicity (desktop application requiring no infrastructure), cost structure enabling bundling economics, and demo effectiveness that created immediate customer engagement. Partners who can articulate this level of specificity about their ISV relationships demonstrate the strategic thinking that produces good customer recommendations.
“Partners who can articulate why they chose their AI ISVs and how those choices align with customer requirements demonstrate the strategic thinking that produces good recommendations. Partners who offer ISV solutions without clear rationale may be selling what they have rather than what you need.”
— John Byron Hanby IV, The AI Strategy Blueprint, Chapter 11
The AI Partner Blueprint: Your Due Diligence Multiplier
For organizations conducting rigorous partner evaluation, The AI Partner Blueprint by John Byron Hanby IV provides a framework that creates asymmetric advantage in the selection process. Written as a comprehensive guide for channel partners building AI practices, the book reveals what excellence in AI partnership looks like from the inside.
When you understand what a well-developed AI partner practice entails, you can evaluate prospective partners against that standard. The topics covered in The AI Partner Blueprint include ISV evaluation methodologies, practice development frameworks, training and certification strategies, and go-to-market execution. Partners who have thought through these elements demonstrate the operational maturity that enterprise relationships require.
The AI Partner Blueprint
Written for channel partners building AI practices — and for enterprise buyers who want to understand exactly what "AI excellence" requires from the partner side. Use it as a self-assessment tool for your current partners or as a standard against which to evaluate new candidates.
- ISV evaluation and selection methodology
- AI practice development frameworks
- Training and certification strategies
- Go-to-market execution playbooks
- Customer engagement and expansion models
For organizations with established IT partner relationships, The AI Partner Blueprint offers a strategic alternative to replacing your current partners with AI-native vendors who lack organizational context. Sharing the book with key IT partners signals your organization's commitment to AI transformation and creates urgency for partners to develop relevant capabilities. Partners who want to maintain and grow the relationship receive a structured approach to capability building that accelerates their readiness to serve your needs.
Tiered Partner Prospect List
Not all partners merit equal evaluation investment. A tiered approach to partner qualification focuses your due diligence resources on the most promising candidates while maintaining a pipeline of alternatives for contingency planning. Apply the 10-point scorecard above before advancing any prospect beyond Tier 2.
Primary Candidates
Partners with documented AI practices, verifiable certifications, completed implementations in your industry, and available references. Score 16–20 on the evaluation scorecard. Invest full due diligence including reference calls, capabilities demonstrations, and joint pilot scoping.
- 2+ years building AI practice
- Elite or premium ISV tier status
- 10+ completed AI implementations
- Industry-specific references available immediately
- Self-sustaining AI services P&L
Secondary Candidates
Partners with credible AI investment signals but gaps in one or two criteria. Score 10–15. Suitable for non-critical AI initiatives or as secondary partners in a multi-vendor environment. Continue monitoring; they may qualify for Tier 1 as their practice matures.
- 12–24 months into AI practice
- Standard ISV tier with active certifications
- 3–10 completed AI implementations
- References available with advance notice
- Services revenue growing but not yet self-sustaining
Emerging / Watch List
Partners showing genuine intent to build AI capability but without the track record to merit critical deployment trust. Score below 10. Not appropriate for primary AI partner status. Suitable for monitoring and low-risk exploratory engagements only.
- Recent AI practice investment (under 12 months)
- Basic ISV tier; certifications in progress
- Fewer than 3 completed AI implementations
- No AI-specific references available
- Products only; no AI services offering yet
For organizations navigating a transition from an AI-washing partner to a genuine one, see our companion article: AI-Washing: The New Greenwashing — And 7 Questions That Expose It. For the broader context of AI strategy within which partner selection operates, the pillar guide is at Enterprise AI Strategy: The Complete Framework.
Channel & Partner Deployments in Production
Real deployments from the book — quantified outcomes from Iternal customers across regulated, mission-critical industries.
Global IT Services Sales Transformation
A global IT services organization deployed AirgapAI to its sales team, transforming proposal creation and competitive research across a distributed workforce.
- Proposal creation time reduced dramatically
- Sales team AI adoption exceeded 80% within 90 days
- Channel partner-led deployment model proven at scale
Sterling Federal Systems Integrator — Gaudi 3
Sterling Federal, a leading federal systems integrator, deployed Intel Gaudi 3 AI acceleration paired with AirgapAI for secure, on-premises AI inference in federal environments.
- FedRAMP-compliant AI deployment at federal agency
- Zero data transmission to external environments
- SI-led deployment model replicable across federal portfolio
Top 5 Financial Institution — IT Asset Management
A top-five U.S. financial institution used AI to transform IT asset management, dramatically reducing the time required for vendor risk assessments and compliance documentation.
- Vendor risk assessment cycle reduced from weeks to days
- AI deployed within existing security perimeter
- Compliance documentation quality measurably improved
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Partner Selection and AI Strategy Consulting
Our AI Strategy consulting programs include partner evaluation, ISV selection guidance, and go-to-market framework development — delivered as a 30-day Sprint or a 6-month Transformation Program.
Frequently Asked Questions
The most predictive single criterion is whether the partner uses AI in their own operations before selling it. Partners who have deployed AI internally understand implementation challenges, adoption barriers, and value realization patterns from direct experience. This operational credibility cannot be manufactured from vendor training materials. Ask specifically how the partner uses AI in their own sales process, proposal generation, and customer research — and probe for details. Vague answers indicate theoretical knowledge, not operational experience.
AI-washing is the channel equivalent of greenwashing — partners who have added AI branding to their marketing without building substantive capability. The diagnostic is specificity: partners who have built genuine AI practices answer evaluation questions with detail drawn from experience (specific engagements, specific challenges, specific outcomes). Partners engaged in AI-washing respond with generalities, reference vendor partnerships without describing implementations, and deflect requests for references. See our full guide: AI-Washing: The 7 Exposure Questions.
The vTECH io case study from Chapter 11 of The AI Strategy Blueprint provides the benchmark: $5–6 million in net new AI revenue within Year 1, approximately 15% growth in total company revenue, 300%+ AI PC sales increase year-over-year, and 11 months to services ROI. These figures reflect a partner with genuine AI practice investment, not a reseller with a new logo on their website. When evaluating partners, ask them to describe their equivalent metrics — what AI-specific revenue have they generated, what customer expansion patterns have they observed, and how long it took their AI practice to become self-sustaining.
According to BCG research cited in The AI Strategy Blueprint, 84% of organizations work with two or more vendors on AI initiatives. The multi-vendor reality reflects the genuine breadth of AI capability required for enterprise transformation — no single partner provides best-in-class capability across infrastructure, model hosting, security, workflow integration, and training simultaneously. The practical guidance from Chapter 11: invest in deep partnership with one primary AI partner (Tier 1) while maintaining one or two secondary relationships for specific capability gaps. Avoid distributing loyalty so broadly that no partner is sufficiently invested in your success.
The AI Partner Blueprint by John Byron Hanby IV is a companion book to The AI Strategy Blueprint, written specifically for channel partners building AI practices. For enterprise buyers, its value is asymmetric: it reveals what excellence in AI partnership looks like from the inside, enabling you to evaluate prospective partners against a documented standard rather than intuition. You can also share it directly with your existing IT partners as a capability development roadmap — signaling your AI transformation commitment while giving trusted partners a structured path to developing the AI capabilities you require. Available at iternal.ai/ai-partner-blueprint.
The ISV Partnership Evaluation Matrix in Chapter 11 covers five dimensions: partnership tier achieved and the requirements to achieve it; number of certified personnel and their specializations; implementation count and industry relevance; reference availability for ISV-specific work; and whether the ISV actively co-sells with the partner (a strong signal of ISV-validated trust). The most revealing question is asking the partner to explain why they chose each ISV they represent — partners with genuine strategic thinking articulate how their ISV choices align with customer requirements; partners without a strategy describe what they were offered.
For regulated industries (healthcare, government, financial services, defense), security evaluation operates at two levels: the partner's own security practices and the architecture of the AI solutions they represent. For the solution architecture, ask specifically about data residency — does the solution transmit data to external environments, or does it process everything within your security perimeter? Solutions like AirgapAI that operate entirely on-device eliminate data transmission risk entirely, a critical requirement for HIPAA, FedRAMP, CMMC, and ITAR compliance. Partners who cannot explain the data architecture of their proposed solutions at this level of detail lack the security fluency that regulated industries require.