Black Mesa GAIP Progress Report: What the Latest Draft Reveals About Industry Direction

The Context: ARPA-H, CATALYST, and Why GAIP

The Advanced Research Projects Agency for Health (ARPA-H) was established in 2022 as a federal agency modeled on DARPA, with a remit to fund high-impact biomedical research with the potential to transform health outcomes. The CATALYST program — Computational ADME-Tox and Physiology Analysis for Safer Therapeutics — is one of ARPA-H’s portfolio programs, focused on computational approaches to making drug development safer and faster. As described on ARPA-H’s CATALYST program page, the program’s broader mission is to fast-track safer medicines from lab to patients through computational methods.

The Black Mesa GAIP award is one of several CATALYST awards, but it is notable because it addresses a regulatory enablement gap rather than a therapeutic or computational research question. As the EIN Presswire announcement of the award explains, the GAIP framework is intended to be modeled after existing Good Practice standards — Good Laboratory Practice, Good Manufacturing Practice, Good Clinical Practice — and designed to protect the safety and efficacy of drugs by preventing the data integrity risks posed by uncontrolled AI use.

The agency-funded, framework-development approach is meaningful. Most regulatory frameworks emerge from regulators themselves: FDA publishes guidance, EMA publishes reflection papers, ICH publishes harmonized documents. The CATALYST funding model places a federally-funded framework into the public domain, intended for adoption by both regulators and industry. This is closer to the NIH model for clinical research methodologies than to a typical regulatory rulemaking, and the practical effect is that the framework is being developed with broader stakeholder input than a regulator-led process would typically include.

Pharma quality leaders who have worked through earlier convergences — the emergence of ICH Q9 on quality risk management, the maturation of ICH Q10 on pharmaceutical quality systems, or the development of GAMP 5 as ISPE’s de facto computer system validation standard — will recognize the pattern. Industry-recognized frameworks often emerge from coordinated work by a credible developer that the regulators then reference and endorse. The Black Mesa GAIP is positioned to play that role for AI in drug development.

What We Know Publicly About the Framework

The publicly available information about the Black Mesa GAIP framework is, as of May 2026, primarily limited to the ARPA-H award announcement, the CATALYST program materials, and Black Mesa’s own public communications about the VERISCAN platform. The framework itself has not been published. What we can responsibly summarize from the public signal:

Scope. The framework is being designed for AI use in drug discovery and development, with the explicit objective of supporting AI models, datasets, and infrastructure that meet 21 CFR data integrity requirements. This positions the framework primarily for sponsor-side use in research and development phases, though it will likely have implications across the lifecycle.

Modeling on existing GxPs. The framework is intended to be modeled after GLP, GMP, and GCP — meaning it will articulate principles, expectations, and verifiable controls that organizations can implement to demonstrate disciplined AI use. This is meaningfully different from a discussion paper or reflection paper; it is intended to be an actionable framework.

Technical methods. Beyond the framework, the Black Mesa work includes “associated technical methods” that help organizations operationalize the framework. The combination of framework plus methods is structurally similar to GAMP 5, which couples principles with operational guidance.

Funding mechanism. The work is federally funded through ARPA-H, which gives the framework a different credibility profile than a commercially developed framework. Federal funding does not guarantee regulatory adoption, but it does signal that the work is being conducted with regulatory uptake as a stated objective.

Connection to VERISCAN. Black Mesa’s existing VERISCAN platform, as described on Black Mesa’s public site, is a software product focused on validation and verification of AI systems. The GAIP framework appears to be a generalization of the validation discipline that underpins VERISCAN into a publicly available standard.

What is not yet publicly known: the precise structure of the framework, the specific control areas it will cover, the timeline for publication, the mechanism by which it will be maintained, and the relationship between the framework and any future FDA endorsement or reference. Quality leaders working from the public signal should be candid about the boundaries of what is known versus what is being inferred.

What the Design Choices Signal About Industry Direction

Even with limited public information, the design choices visible in the award and CATALYST materials signal several things about the direction the field is moving.

GxP framing is being normalized for AI. The decision to model the framework on existing Good Practice standards reflects a broader convergence: the regulatory community is treating AI as a discipline that requires GxP-equivalent rigor, not as a special case requiring entirely new frameworks. Pharma quality leaders who have been extending their QMS to AI under “GAIP” or “Good AI Practice” labels are aligned with where the formal framework is going.

Data integrity is the central organizing principle. The framework’s explicit anchor to 21 CFR data integrity requirements signals that data integrity — not model performance or model bias alone — is the organizing principle the FDA will recognize. This aligns with the FDA’s longstanding posture on ALCOA+ data integrity, extended to AI-generated and AI-consumed data.

Sponsor responsibility is preserved. The framework is being designed for organizational adoption, meaning the sponsor remains responsible for the validated state of AI systems even when vendor-provided components are involved. This is consistent with the FDA’s broader posture: vendors provide capabilities, sponsors validate use.

Drug discovery scope matters. Most existing AI compliance work has focused on clinical, manufacturing, and submissions phases. Extending GAIP framing into drug discovery — where AI use is most pervasive and most lightly regulated today — signals that the FDA’s eventual posture will not exempt discovery-phase AI from data integrity expectations, particularly where discovery outputs feed into IND or NDA submissions downstream.

Technical methods alongside the framework. The decision to develop technical methods alongside the framework — rather than publishing principles in isolation — reflects industry feedback that abstract principles without operational guidance produce inconsistent implementation. The GAMP 5 model, with its principles plus appendices for specific technology classes, is the closest analogue, and the structural similarity is probably not accidental.

The 21 CFR Anchor and Why It Matters

The Black Mesa GAIP framework’s explicit anchor to 21 CFR data integrity requirements is the single most consequential design decision visible in the public information. Three reasons.

First, it situates the framework within the FDA’s existing regulatory architecture rather than alongside it. Quality leaders applying the framework will be applying it through their existing 21 CFR Part 11 disciplines, their existing CSV practices, and their existing audit trail expectations — extended to AI. This is materially easier to operationalize than a parallel framework would be.

Second, it aligns the framework with FDA’s enforcement posture. FDA inspectors are deeply familiar with 21 CFR Part 11 enforcement; AI-related findings that map onto Part 11 categories will be readable to inspectors without specialized AI training. This is the practical concern that many quality leaders have raised about AI inspection readiness: how do you produce documentation that an inspector who has not been trained on AI can evaluate? The 21 CFR anchor provides a partial answer.

Third, it preserves the audit trail and reproducibility expectations that underpin pharma’s data integrity disciplines. AI systems that comply with 21 CFR through GAIP will produce documented evidence of data provenance, model lineage, decision rationale, and change history. These are the categories inspectors probe, and they translate directly across deterministic and AI-based systems.

What this means for quality leaders: even without the published framework in hand, the operational direction is recognizable. AI use cases in pharma should be governed by extensions of existing 21 CFR-aligned QMS disciplines, not by parallel AI-specific disciplines. Quality teams that have been pursuing this direction are well-positioned for the GAIP framework when it publishes.

Two GAIPs: Disambiguating the Term

An important point of clarity: the term “GAIP” is being used in two distinct senses in current industry conversation, and they are not the same thing.

GAIP as the FDA/EMA joint principles. The FDA and EMA have published “Guiding Principles of Good AI Practice for Drug Development,” a set of joint principles articulated in January 2026 that cover human-centric design, risk-based approach, adherence to standards, clear context of use, multidisciplinary expertise, data governance and documentation, model design and development practices, risk-based performance assessment, lifecycle management, and clear essential information. These principles, available on the FDA Guiding Principles page, are a regulator-issued framework that articulates expectations rather than detailed controls.

GAIP as the Black Mesa framework. The Black Mesa GAIP framework is a federally-funded standard being developed under ARPA-H, intended to provide the detailed controls, technical methods, and operational guidance that operationalize principles like the FDA/EMA ones.

These two GAIPs are complementary rather than competing. The FDA/EMA principles articulate what good AI practice means; the Black Mesa framework articulates how to operationalize it. Quality leaders working in pharma will encounter both, and the distinction matters because the two have different statuses (regulator-issued versus federally-funded industry standard), different scopes (principles versus controls), and different mechanisms for evolution.

Open Questions and What to Watch For

Working from the publicly available information, several open questions will materially shape how the Black Mesa GAIP framework lands.

Timeline. ARPA-H program awards typically run on multi-year timelines. CATALYST as a program is multi-year, and the GAIP work is one of several awards within it. The framework is unlikely to publish in a finalized form before late 2026 or 2027, though interim publications and engagement with industry stakeholders are likely throughout that window.

Regulator engagement. The degree to which the FDA, EMA, and other regulators formally engage with or endorse the framework will shape its uptake. ARPA-H funding does not automatically translate to regulatory endorsement; the framework’s adoption will depend on how the regulators choose to reference it once published.

Industry uptake. Beyond regulator endorsement, the framework’s uptake will depend on whether major pharma companies adopt it as a reference for their internal QMS. Early adopter signals will be visible through industry working groups, ISPE engagement, PDA workshops, and similar fora.

Scope expansion. The framework is being developed for drug discovery and development, but the underlying disciplines apply across pharma. Whether the published framework is generalized to manufacturing, pharmacovigilance, and post-market surveillance will affect its operational relevance.

Maintenance mechanism. AI moves faster than typical regulatory documents. The framework’s maintenance mechanism — whether it is maintained by Black Mesa, by ARPA-H, by an industry consortium, or by a standards body — will affect its long-term viability.

These open questions are reasonable for quality leaders to monitor through normal channels (ARPA-H publications, ISPE engagement, PDA workshops, regulatory comments) rather than to speculate about. The discipline of distinguishing between what is known, what is being inferred, and what is open is important when working with frameworks that have not yet published in full.

How Pharma Quality Leaders Should Prepare

Even without the published framework in hand, several actions are defensible.

Extend QMS to AI under the existing 21 CFR architecture. The Black Mesa GAIP framework will anchor in 21 CFR. Quality teams extending their QMS to AI under existing 21 CFR Part 11, CSV, and data integrity disciplines are aligned with where the framework is going. This work does not require waiting for the published framework.

Align with the FDA/EMA principles now. The ten principles in the published FDA Guiding Principles document articulate what regulators expect at a level of generality that quality leaders can act on immediately. Documentation aligned with these principles will likely also align with the eventual Black Mesa framework.

Track ARPA-H and CATALYST publications. ARPA-H publications, including the ARPA-H AI-enabled medical tools program announcements, provide early signal about where the framework is heading. Quality leaders should follow these directly rather than relying on secondary commentary.

Engage with industry working groups. ISPE, PDA, and similar bodies are sites where the framework will be discussed and operationalized in advance of full publication. Active engagement gives quality leaders early access to the operational thinking that will shape the published framework.

Avoid overcommitting to specifics. The framework has not yet published. Quality leaders who publicly commit their organizations to specific compliance positions based on inferred framework content risk having to walk those positions back when the actual document publishes. Stating direction is fine; stating specifics is premature.

The pattern with emerging frameworks is consistent: the organizations that act on the recognizable direction early, while maintaining flexibility on specifics, end up in materially better positions than organizations that either wait passively for the final document or commit prematurely to specific operational positions. The Black Mesa GAIP framework appears likely to reward the same posture.

Why the federally-funded model matters strategically

One dimension worth understanding more deeply is what the federally-funded model implies for how the framework will be maintained over time. Commercially developed frameworks (such as those produced by individual vendor consortia) are vulnerable to commercial pressures: vendors may evolve the framework in ways that favor their products, or the framework may stall when funding dries up. Regulator-developed frameworks are vulnerable to political and administrative cycles: leadership changes can shift priorities, and the regulator’s bandwidth is finite. Federally-funded frameworks developed by an independent organization sit between these two models. They benefit from federal credibility and stable funding for the development phase, but their long-term maintenance depends on the model’s adoption and ongoing investment.

For pharma quality leaders, the implication is that the Black Mesa GAIP framework’s eventual maintenance model will be important to understand. A framework that publishes once and then atrophies provides less long-term value than a framework with a clear maintenance mechanism. The publicly available signal does not yet articulate the maintenance model in detail, and this is one of the open questions worth tracking as the framework develops.

The intersection with industry standards bodies

Beyond the federal framework itself, an important strategic dimension is how the framework will interact with industry standards bodies — particularly ISPE through its GAMP work, PDA through its technical reports, and RAPS through its regulatory affairs guidance. The most efficient outcome for the field would be alignment between the Black Mesa GAIP framework and the operational guidance produced by these bodies. Quality leaders should watch for joint engagement between Black Mesa and these standards organizations during the framework’s development; substantial joint engagement would signal that the eventual published framework will integrate with existing industry standards rather than competing with them.

The PDA’s September 2025 workshop on AI in pharma — which included sessions on validation, oversight, and the comparison between EU and US draft regulations — represents the kind of forum where this integration is most likely to be discussed. Quality leaders engaging with these workshops gain early visibility into how the framework will likely interact with industry practice once published, and the engagement itself provides input that shapes the framework’s eventual form. The opportunity to influence the framework is greatest during the development window; once published, the framework will be much harder to modify.

Reading the framework against the FDA’s 2025 enforcement posture

A final strategic point worth understanding is how the framework will likely interact with FDA’s evolving enforcement posture on AI. The FDA’s enforcement actions on AI-related findings — including 483 observations and warning letters that reference AI or computerized system deficiencies — provide ongoing signal about what the agency considers a substantive failure versus a minor finding. Quality leaders monitoring these enforcement actions can anticipate where the Black Mesa GAIP framework will most likely focus its detailed controls. The framework’s value will be greatest precisely in the areas where FDA enforcement has been most active, because those are the areas where the cost of a finding is highest.

The publicly available enforcement signal suggests that data integrity, audit trail completeness, change control discipline, and human oversight evidencing are the areas of most active FDA scrutiny. These are also the areas the Black Mesa GAIP framework, anchored in 21 CFR, is most likely to address in detail. Quality leaders aligning their preparation work with these areas will be aligning simultaneously with the framework’s likely focus and with the FDA’s current enforcement posture — a doubly defensible position.

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