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Operating Models

Principles do not field themselves: why I am writing RAIDops

I did not set out to write a book.

RAIDops began during my Master of Science studies in Columbia University's Information & Knowledge Strategy program. An independent study, guided by Blake M. DiCosola III and strengthened by the advice and input of Edward J. Hoffman, started as a literature review of trustworthy artificial intelligence in national defense. Blake and Ed are both IKNS faculty; Ed previously served as NASA's first Chief Knowledge Officer.

The review grew into a long paper. The paper kept returning to an unresolved organizational problem. Eventually, the problem outgrew the paper.

I invented RAIDops and coined the name Responsible AI Development Operations for the framework that emerged from that work. The working monograph is coauthored with Blake and Ed, whose substantive intellectual contributions, guidance, editing, and mentorship have materially shaped it.1 Their collaboration has made the work substantially better.

I have hesitated to write publicly about RAIDops because the research program is active and the manuscript is unfinished. I am not going to reproduce the complete pattern catalog, assessment instruments, or the book's full analytical machinery here. But a framework concerned with reviewability should itself be open to review. This essay offers the public argument: enough to explain and defend RAIDops, while preserving the monograph as the place where the complete derivation, architecture, patterns, evidence controls, and limitations belong.

The thesis is straightforward: trustworthy AI is not merely a property to test in a model. It is an operating achievement that an organization must repeatedly produce, challenge, bound, preserve, and sometimes revoke.

That problem is not abstract to me. It recurs across my work in enterprise AI engineering, knowledge systems, and public-sector technology: building a capable system is only part of the job. The institution must also keep the purpose, evidence, authority, and means of intervention intact as the system crosses teams, contracts, environments, and time.

The CMMC suspension is an organizational knowledge test

Cybersecurity certification was never going to be just another audit for the companies that build and support U.S. defense systems. Preparing for it changed budgets, architectures, hiring plans, subcontractor relationships, and the everyday work connecting cyber teams with engineers, contracts staff, and program leaders.

Then, on July 13, 2026, the Department of Defense (DoD) suspended the planned transition to Phase II of the Cybersecurity Maturity Model Certification (CMMC) program. The Department also halted later implementation milestones and opened a 60-day review of the program.

The announcement brought meaningful relief from the coming expansion of third-party assessments. It did not, however, abolish CMMC or erase the contractual duty to protect defense information. Phase I continues, and the underlying safeguarding and reporting requirements remain in force.

That leaves defense organizations with a harder question than whether to keep preparing for an assessment: Which parts of the CMMC effort were merely certification overhead, and which parts became necessary organizational capability?

The distinction matters because a compliance program leaves behind more than policies and evidence. It also changes who talks to whom, how work moves between teams, where decisions are recorded, and whether an organization can accurately explain the security of its systems. Those capabilities are slow to build and surprisingly easy to lose.

Financial agents will be proven in the exception queue

Anthropic has released agent templates for financial services, including work such as preparing pitchbooks, screening Know Your Customer (KYC) files, reviewing valuations, reconciling ledgers, and supporting the monthly close.

These are not toy tasks. They sit inside governed processes with source systems, deadlines, approvals, materiality judgments, and audit expectations. Their automation will be judged less by the clean case than by what happens when the evidence does not line up.

Enterprise AI scales through the operating model

OpenAI's account of the next phase of enterprise artificial intelligence describes rapid growth in organizational use and increasing demand for agents that can operate across real workflows. The direction is clear: companies are moving beyond isolated conversations toward systems that research, update records, create artifacts, and complete multi-step work.

That movement makes the operating model—not access to the model—the limiting factor.

Agent security starts before the agent acts

The National Institute of Standards and Technology (NIST) has opened a request for information on securing artificial intelligence agent systems. The timing is right. Organizations are moving from systems that generate suggestions toward systems that can call tools, manipulate records, send messages, and coordinate multi-step work.

The security question is no longer only what a model might say. It is what the complete system is allowed to do—and whether the organization can reconstruct what happened afterward.

Governance needs an evidence system

The new year has opened with a practical test for artificial intelligence governance. California's Transparency in Frontier Artificial Intelligence Act (TFAIA), enacted through Senate Bill 53 (SB 53), is now operative. It asks covered frontier developers for published frameworks, safety reporting, incident processes, and protections for employees who raise serious concerns.

The particulars apply to a defined group of companies. The lesson travels much further: a governance commitment is only as real as the evidence an organization can produce when somebody asks how the commitment works.

AI security overlays can connect two professions

The National Institute of Standards and Technology's (NIST) August 14 concept paper proposes security-control overlays tailored to artificial intelligence (AI) systems. The work adapts controls from NIST Special Publication 800-53 to generative, predictive, single-agent, multi-agent, and developer use cases.

The practical promise is not a new checklist. It is a better conversation between two professions that too often approach the same system with different maps.

An AI action plan becomes real at the handoff

The White House's July 23 release of America's Artificial Intelligence (AI) Action Plan outlines more than 90 actions across innovation, infrastructure, international engagement, and security.

Any plan of that scale contains choices people can debate. The implementation lesson is less partisan and more practical: strategy succeeds or fails in the handoff from an announced action to an accountable operating system.

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