The Hacker News ran a piece this week on “shady AI” as security’s next big governance problem, and the framing is right even if the nickname is a little cute. The short version: employees are using AI tools nobody sanctioned, nobody logged, and nobody can audit, and security teams are discovering it the same way they discovered shadow IT fifteen years ago. After the damage.

I want to push on one part of that story, because I think the industry keeps drawing the wrong lesson from it.

We’ve seen this movie, and we handled it badly the first time

Around 2011, Dropbox showed up on every corporate laptop in America without a single procurement meeting. IT departments responded with the only tool they had: blocking. Employees responded the way employees always do. They found another sync tool, then another, then they just emailed files to their personal Gmail. The data left anyway. It just left through channels nobody could see.

Shadow AI is that story with higher stakes and a faster clock. A paralegal pastes a client’s settlement draft into a free chatbot to “tighten the language.” An analyst uploads the quarterly pipeline to get a summary slide. A developer feeds proprietary code into a coding assistant on a personal account because the corporate one has a waiting list. None of these people think they’re exfiltrating data. They think they’re doing their jobs faster, and honestly, they are.

Here’s the uncomfortable part. When you block the tool without offering a sanctioned alternative, you don’t stop the behavior. You just push it onto personal devices and personal accounts, where your DLP tooling, your logs, and your retention policies mean exactly nothing.

The real gap is that nobody owns this

Read the incident reports behind these stories and a pattern shows up fast. It’s rarely a technology failure. It’s an ownership failure.

Ask a mid-sized firm “who owns AI here?” and you’ll get one of four answers:

1. The CISO, who treats it purely as a threat surface and defaults to no
2. The CIO, who treats it as a procurement question and defaults to whatever the incumbent vendor bundles in
3. A volunteer “AI champion” with enthusiasm and no authority
4. Nobody

All four produce shadow AI. The CISO’s blanket “no” produces it fastest, because demand doesn’t disappear when you deny it. Demand goes underground.

What actually closes the gap is a role most companies under a few thousand employees can’t justify hiring full time: someone with the authority to say yes to specific tools under specific conditions, the technical depth to evaluate what a vendor actually does with your prompts and outputs, and the standing to write policy that survives contact with the legal department. A Chief AI Officer, in other words. Which is exactly why the fractional version of that role exists, and why we’ve built our practice around it.

What “sanctioned” actually looks like

A fractional CAIO engagement done right isn’t a policy PDF that ships in week two and dies in a SharePoint folder. It’s a working AI Program Office, even a small one, and it does a few unglamorous things well.

It builds an inventory first. You cannot govern what you can’t see, and most organizations are shocked at their own numbers. Egress logs and expense reports tell the truth: the tools are already in the building. Start from reality, not from the org chart’s fiction.

Then it gives people a legitimate path. An approved model catalog, provisioned accounts with enterprise data terms, and clear rules about what data classes can touch what tools. When the sanctioned option is good and available, the shadow option loses most of its appeal. People weren’t choosing the risky tool because they loved risk. They were choosing it because it was the only one that worked.

Then it keeps watching. Usage dashboards, periodic review of what’s flowing where, an actual human accountable when the answer to “where did that document go?” needs to be produced for a regulator or a client. Governance is a verb. You do it continuously or you’re not doing it.

And underneath all of it sits an architectural question the article gestures at but doesn’t quite land: where does the model run? For a lot of the data that leaks through shadow AI, the honest fix is not a better vendor contract. It’s inference that runs on infrastructure you control, where prompts and outputs never leave your environment and no third party’s retention policy is part of your threat model. Your data, your rules, and that includes the AI working on it. Your AI, your rules. Governance you can’t verify at the infrastructure level is governance you’re taking on faith.

The window for doing this cheaply is now

Every month of unmanaged use is training data you can’t claw back, client information sitting in some vendor’s logs under terms nobody read, and habits getting harder to redirect. The organizations that treated shadow IT as a demand signal in 2012, and built sanctioned answers, came out with better tooling and fewer breaches than the ones that fought a blocking war for five years. Same fork in the road here, shorter runway.

So here’s what I’d genuinely like to know from anyone running security or ops at a firm right now: have you actually looked? Pulled the egress logs, checked the expense reports, asked the question in an all-hands where people could answer honestly? And if you did, what did you find versus what you expected? Tell me below. The gap between those two numbers is the whole story.

The post Shadow AI isn’t a user problem. It’s a missing-owner problem. appeared first on Modular Technology Group.

Most companies did not choose an AI strategy. They chose a model, and the strategy grew up around it.

A piece on Towards AI this week, The Durable Asset is the Control Plane, not the Frontier Model, puts that habit on trial. The author’s evidence is Cursor, the coding tool that became one of Anthropic’s largest customers while Anthropic’s own coding agent increasingly competed with it at the application layer. Cursor meanwhile built upward: its own models, its own agent runtime, and in July a router that decides which model sees each request. The article’s conclusion is blunt: the durable asset is the control plane above the model, the layer that holds policy, spend, identity, and evaluation so no single vendor can hold them for you. The model itself is replaceable.

At Modular Technology Group we agree, and we would push it one step further for the companies we work with. That layer is where “your data, your rules” has to live, because the rules do not survive anywhere else.

The layer nobody bought

The article describes four layers. Models are the specialists. A router decides which specialist sees which request. A harness is the runtime around the model: tools, files, memory, permissions, the loop that turns tokens into actions. And a meta-harness sits above all of that so harnesses and models can be swapped without rewriting anything. Databricks has open-sourced one, and its hosted version is in beta as of this month.

The author’s analogy is a hospital. Nobody sends every patient to the most expensive specialist. Primary care handles the predictable cases, triage decides who goes where, and the hospital system supplies identity, budgets, records, and oversight across all of it. Then comes the line worth taping to a monitor: most companies bought a specialist and never built a hospital.

Picture a forty-person firm. Someone signed up for a frontier chat tool two years ago. It worked, so more people used it. Prompts now carry client matters and payroll data. Nobody can say what it costs per month across the seats, which model version answered a given question, or what happens to the account when the vendor ships a product that competes with the firm’s own service. There is no router, since there is only one model, and the policy is whatever the vendor’s terms of service say this quarter. The firm has an excellent specialist and no hospital.

Routing is a governance decision wearing an engineering hat

It is tempting to read the article as advice for platform teams at large companies, since the examples are Cursor, Snowflake, Microsoft, and NVIDIA. The routing products those companies ship are real, and the article is careful about their fine print: some routers require a specific model to be enabled, and curating a model list can shrink your effective context window to the smallest model in the set. The author’s advice is to ask every routing vendor which models are mandatory before assuming the layer is provider-neutral.

But strip the vendor names away and routing is a question a small firm can answer today. Which of our work is routine and easy to check? Which of it is novel, or high enough stakes that a person should sign off? The first bucket can run on a validated open-weight model on hardware you control, at a fixed cost, with the data never leaving the building. The second bucket earns the frontier model. The third bucket, the high-stakes one, gets verification and a human approval regardless of which model answered.

Odds are your firm has never measured its own split. That measurement is where a governance engagement should start, and the point of doing it first is that nobody buys anything until the split is on paper. The article’s phrase for the payoff is refusing to spend frontier dollars on work a cheaper, validated model already handles. Read that as a policy statement rather than a cost tip, because once the routing rule enforces it, the policy holds without anyone re-reading a memo.

Your data, your rules, and that includes the AI working on it. Your AI, your rules. The router is where the second half of that gets enforced.

What to own, and what to rent

The article closes with a list of what the durable asset actually contains: models that can change without a rewrite, routing that can learn, harnesses that can be substituted, policies that follow the workload, identity and budgets that stay centralized, and evals that prove the system is improving. It also notes that only operationally mature teams pull this off, because you need evals that say which work is safe to route cheaper, and policy that is not a prompt.

For a small or mid-sized firm, that list is a buy-versus-own map. Rent the specialists. As the article says, models will keep changing and prices will keep moving, so there is no reason to be loyal to one. Own the hospital. The gateway that every request passes through, the budget that caps a runaway agent, the log that says which model answered which question with whose data, and the routing rule that keeps client records on infrastructure you control. Those are small pieces of software. They are also the only pieces you cannot afford to have a vendor hold for you. When the vendor’s incentives shift, that layer is what lets you leave on your own terms.

You do not need Databricks or a platform team to get there. You need someone accountable for the question, a private environment where routine work can run, a gateway in front of everything else, and the discipline to measure cost per finished task rather than cost per token. That is the stack we design for firms that are too small for an enterprise AI program and too regulated to keep improvising, and the order of operations is the whole method. Policy first. Then routing. Then, and only then, the model.

So here is the question for your own shop. If your frontier vendor announced tomorrow that it now sells what you sell, which parts of your AI setup would you keep, and which would you be renting back? Tell me what that inventory looks like at your firm.

The post Pick the model last appeared first on Modular Technology Group.

Google spent the better part of a year telling everyone that Antigravity, its agentic coding environment, was the future of software work. Agents that plan, write, test, and ship with a human somewhere in the loop, loosely defined. Last week The Register reported that Google is now tethering Antigravity to its enterprise admin controls. Policy enforcement, permission scoping, audit visibility, the works.

Read that again. The company with more AI research muscle than almost anyone on the planet looked at its own autonomous agents running inside customer environments and decided they needed a leash.

That’s not a product announcement. That’s an admission.

What the tether actually tells you

For roughly two years the agentic AI pitch has been velocity. Let the agent touch the repo, the ticket queue, the database, the deployment pipeline. Trust the model. Move fast.

Enterprises, it turns out, were not buying it at scale. Security teams kept asking the same unglamorous questions. What can this agent read? What can it write? Who approved that? Where’s the log? And the honest answer, for most agentic tools shipped since 2024, was some version of “we’re working on it.”

So Google did what a vendor under pressure does: it built the controls its buyers were demanding and wired them into the Google admin plane. Good for Google. Genuinely good for Antigravity customers, too, in the narrow sense. An agent with scoped permissions and an audit trail beats an agent without them every single day.

But notice where those controls live. Inside Google’s console, expressed in Google’s policy language, enforced by Google’s infrastructure, covering Google’s agents. If you run Antigravity plus a Copilot deployment plus a homegrown agent on an open-weights model, and most mid-size shops we talk to are already in exactly that mixed state, you now have one well-governed island and a lot of open water.

Governance rented from a vendor is still lock-in

Here’s the part that should bother you more than the ungoverned agents did.

When your AI governance is a feature of one vendor’s platform, your policy layer becomes a switching cost. Every rule you write in their console, every approval workflow you build around their audit log, every compliance attestation that cites their controls, all of it deepens the moat around that one product. Leave the product and you leave your governance behind. You’d have to rebuild it from scratch inside the next vendor’s console, in their language, with their gaps.

That’s a strange place to park something as load-bearing as “who is allowed to let software act on our behalf, and under what conditions.”

Governance isn’t a feature. It’s a function. It belongs to the organization, above the tools, the way your security policy isn’t a setting inside your firewall vendor’s dashboard. The firewall enforces the policy. It doesn’t own it.

What owning it looks like

This is exactly the problem a fractional Chief AI Officer, or a small AI Program Office if you want the standing version, exists to solve. Not a committee that meets quarterly and produces a PDF. A working function that does specific things:

• Keeps an inventory of every agent and model in use, sanctioned or not. Most orgs that build this list for the first time find two or three tools nobody in leadership knew about.
• Writes permission and data-access policy once, in plain language the business owns, then maps it onto each platform’s controls. Google’s tether becomes one enforcement point among several, instead of the whole strategy.
• Defines what gets logged, reviewed, and escalated when an agent acts, regardless of whose agent it is.
• Maintains an exit plan per vendor. If the policy layer is yours, swapping the tool underneath is an engineering project, not an identity crisis.

None of this requires a full-time executive salary, and at most firms it shouldn’t get one. It requires a few disciplined days a month from someone who has done it before and answers to you, not to a platform’s roadmap.

The alternative is what we’re watching play out now: each hyperscaler ships its own governance surface, each one slightly different, and enterprises stitch together a compliance story out of four vendors’ screenshots and hope the auditor doesn’t ask how the pieces relate.

The principle underneath

Modular’s position on data has been the same since day one. Your data, your rules. And that includes the AI working on it. Your AI, your rules. An agent that can read your files, write your code, and act in your name is not a productivity toy. It’s an actor inside your business, and the rules governing an actor inside your business should be written by you, enforceable everywhere, and portable to whatever stack you run next year.

Google tethering Antigravity is a milestone worth marking, because it settles the argument. Ungoverned agentic AI is untenable, and now even the people selling the agents say so out loud. The open question is only who holds the tether. The vendor, or you.

We think the answer is obvious, and we think it’s a solvable, medium-sized project rather than a moonshot. Most of the firms we work with get a real inventory, a written policy, and enforcement mapping in place within a quarter.

So here’s what I’m curious about. If an auditor walked in tomorrow and asked you to list every AI agent with write access to something that matters in your business, how long would that list take to produce, and who in your org would have to produce it? Tell me honestly. The answers I’ve heard so far range from “ten minutes” to “we’d have to send an all-staff email,” and the gap between those two companies is the entire story.

The post Google put a leash on Antigravity, and that’s the whole story appeared first on Modular Technology Group.

No news this week. One argument instead, built from the ground up, because it is the place our reporting keeps landing and it has earned a hearing on its own. AI governance, for a business your size, is four things. An owner. A boundary. A log. And evidence. That is the entire list.

In this episode

• Who owns this? Not who bought it, and not who is good with computers: who is accountable when it goes wrong. Software with no owner does not sit still, it spreads. The fix costs nothing. One name next to one tool, and it can be a name already on your organization chart.
• Where does it end? A boundary is not a fence around the tool. It is a sentence about your material: what this is allowed to touch, and what it is not. Two lines is a real boundary. Then the harder half: where does your material go while the tool is looking at it? If it has to stay inside your walls, the tool has to run inside your walls.
• What happened? The log, and the one design choice that decides whether it is worth keeping: give the software its own identity. If your AI runs on an employee’s credentials, then in your own logs the software and the employee are one actor, and you cannot answer the only question anybody asks afterward.
• Can you show it? Not describe it. Show it. The name of the owner, the sentence about the boundary, a month of the log. Evidence is cheap to keep and impossible to build backwards. Nobody has ever manufactured last quarter.

The question we asked

Pick any AI tool in your business and answer question one out loud. Who owns this? If a name comes easily, you are further along than you think. If it does not, you have just found this month’s work. Get the Modular Briefing by email and reply to it, or write to Arthur at arthur@modtechgroup.com. A person reads every reply.

A note on this edition

This edition cites no news and no outside sources, on purpose. It is the argument the news editions keep arriving at from different directions, made directly. Nothing in it goes stale.

A note on the voices

Laura and Arthur are AI-generated voices, produced locally on Modular’s own infrastructure. The editorial judgement and script are the work of the Modular team.

Transcript

Your data, your rules.

The post The Modular Briefing, September 3, 2026: What Governance Actually Asks For appeared first on Modular Technology Group.

The Hacker News ran a piece this week on “shady AI”, their sharper name for shadow AI, and called it security’s next big governance problem. They’re right about the diagnosis. I want to push on the cause, because I think most companies are misreading it.

The standard story goes like this: employees discovered that free AI tools make their jobs easier, IT hasn’t sanctioned any of them, so people quietly paste client contracts, patient notes, and source code into whatever chatbot loads fastest. Security finds out months later, usually by accident. Cue the panic memo and the blanket ban.

That story treats shadow AI as a discipline problem. It isn’t a discipline problem. It’s a vacancy.

Nobody owns the question

Walk into a mid-sized firm and ask a simple question: who decides which AI tools are allowed here? Watch what happens. IT says they handle security review but not AI strategy. Legal says they’ll weigh in on contracts when asked. The COO says it’s on the roadmap. Meanwhile a paralegal has a browser extension summarizing privileged email, a sales rep wired a free agent into the CRM with a personal API key, and an analyst is running quarterly numbers through a consumer chatbot that trains on inputs by default.

None of those people are being reckless in their own minds. They’re being productive. The tool worked, nobody said no, and more to the point, nobody was there to say no. Or yes. The chair where that decision should get made is empty at most organizations, and shadow AI is simply what fills a vacuum.

Bans don’t fix vacuums. A ban is a “no” issued by someone who wasn’t in the room when the work needed doing, and employees treat it accordingly. The Hacker News piece touches on this: prohibition pushes usage underground, off the corporate network, onto personal devices, where security has zero visibility instead of partial visibility. You traded a manageable problem for an invisible one and called it policy.

What actually fills the chair

This is the exact gap the fractional Chief AI Officer exists to close. Not a consultant who writes a 40-page acceptable-use policy and leaves. Someone who sits in the seat, part-time but genuinely accountable, and does four unglamorous things.

First, inventory. You can’t govern what you can’t see. Before any policy discussion, find out what’s actually in use: the browser extensions, the personal subscriptions, the API keys nobody logged. Most firms are shocked by this list. The shock is useful. It converts an abstract worry into a spreadsheet with names on it.

Second, sanctioned alternatives. Here’s the part the ban-first crowd skips. People adopted these tools because the tools work. If you take away the free chatbot without replacing the capability, they’ll find another free chatbot. The answer is a private AI workspace they can actually use, running on infrastructure you control, where the drafting and the summarizing and the analysis happen without the data ever leaving your jurisdiction. At Modular that’s not theoretical. Our own teams run daily on the same private stack we deploy for clients, hosted on US soil, at a fixed monthly price, so the finance conversation is as boring as the compliance conversation. Boring is the goal.

Third, a real AI Program Office. Small, standing, cross-functional. It reviews new tool requests in days instead of quarters, keeps the approved list current, and gives employees a place to ask “can I use this?” and get an answer before they’ve already used it. Speed matters more than people admit. An approval process that takes six weeks is a prohibition wearing a lanyard.

Fourth, policy that says yes with conditions. “AI is banned” and “AI is fine, go nuts” are equally lazy. The workable version reads more like: this class of data can go into these tools, this class cannot, here’s the sanctioned path for the gray areas, and here’s who to ask when you’re not sure. Employees follow rules they can actually follow.

The sovereignty thread underneath

There’s a reason this maps so cleanly onto how we think about infrastructure. Your data, your rules. That’s been the thesis all along, and shadow AI is what it looks like when the rules never got written. Every unsanctioned tool is a small, silent transfer of control: your client’s contract now lives in someone else’s training pipeline, under someone else’s terms of service, in someone else’s jurisdiction. And that includes the AI working on the data, not just the data itself. Your AI, your rules. Governance without agency over the models and the tooling is just paperwork about someone else’s decisions.

The firms that get ahead of this won’t be the ones with the strictest bans or the longest policies. They’ll be the ones where somebody actually owns the question, where the sanctioned path is easier than the shadow path, and where “which AI touched this data” has an answer you could give a regulator without sweating.

The empty chair is the whole problem. Filling it doesn’t require a full-time executive salary. It requires deciding that the question deserves an owner.

Here’s what I’d genuinely like to know from the people reading this: when you last found an unsanctioned AI tool inside your organization, how did you find it? An audit, an incident, or dumb luck? The answers to that question tell you more about your governance posture than any policy document will.

The post Shady AI is what an empty chair looks like from the outside appeared first on Modular Technology Group.

Three different authorities, in two different countries, inside ten days. A bar association, a securities examiner and a national cyber agency. Not one of them asked whether anybody’s AI was any good. All three asked the harder question: can you show your work?

In this episode

• The default setting is the policy. The New York City Bar Association released Formal Opinion 2026-2 on August 17 (the opinion itself is dated August 5), extending two earlier opinions from clients to co-counsel, opposing counsel, witnesses, prospective clients and a firm’s own investigators. Get consent from every participant before an AI captures a conversation, and absent a good reason, the default should be to leave it switched off. The duty of competence now includes knowing how to turn the thing off. A New York City opinion rather than a national rule, but the reasoning travels. Formal Opinion 2026-2
• What examiners want is the minutes. Financial Advisor Magazine, working from an examination request document obtained by Citywire, reports what SEC examiners are asking investment advisers about AI: does the firm have an AI committee, does it keep written minutes, which channels promote the firm’s AI (the “AI washing” probe, with two 2024 cases settled for a combined $400,000), and were employees ever trained on the policy. No rule requires a document titled “AI policy”; advertising, compliance, privacy, recordkeeping and fiduciary rules already do the work. The ask is evidence, not a binder. Financial Advisor Magazine
• Agent governance, written down at last, and it is four controls. The UK’s National Cyber Security Centre published interim guidance on agentic AI on August 20. Every agent gets its own identity in a class distinct from humans, credentials scoped and short-lived, a named individual accountable for its activity, and its actions logged into monitoring the way a person’s are. Their maturity ladder ends, for the most sensitive work, at no external access with the model hosted locally. Interim guidance from a British agency, not American law. NCSC

The question we asked

Pick the newest AI tool inside your business. Can you name the person accountable for it, and show what it did last week? Get the Modular Briefing by email and reply to it. A person reads every reply.

A note on the voices

Laura and Arthur are AI-generated voices, produced locally on Modular’s own infrastructure. The reporting, editorial judgement and script are the work of the Modular team. Facts and figures are drawn from the linked sources; check them there before acting on anything.

Transcript

Your data, your rules.

The post The Modular Briefing, August 22, 2026: Show Your Work appeared first on Modular Technology Group.

AI Governance, fractional CAIO, AI program office, ~5 min read

There is a moment that arrives a few weeks after a good governance document lands on your desk. You have read it. You agree with all of it. The five principles are sensible, the risk tiers make sense, and you cannot name a single person whose job it is to do any of it by Friday.

That moment is where most AI governance programs quietly stop.

On 29 July the Society of Pension Professionals published Governance in the Age of AI: A Practical Framework for Responsible Leadership, aimed at pension scheme trustees and the advisers and administrators who serve them. Finextra covered the launch the same week. If you do not work in pensions, read it anyway. Almost nothing in it is specific to pensions, and the argument it makes is the one every department head is about to have.

What the pensions industry actually said

The paper’s central claim is refreshingly unglamorous: AI does not need a new governance regime. It needs the duties that already exist, applied to facts those duties were not written for.

Trustees already owe a duty of prudence. They already have risk registers, service provider oversight, internal controls, and cyber and data governance frameworks. The SPP’s position is that AI belongs inside all of those, not in a separate binder next to them. The framework sets out five principles: transparency, accountability, proportionality to risk, security by design, and meaningful human oversight. It recommends classifying every AI use as low, medium, or high risk, and keeping an AI register that records current use cases, who owns each one, how it was approved, how often it is reviewed, and what gets reported to the board.

The urgency is in the adoption curve. The SPP’s own annual surveys found 87% of pension firms using AI in 2025 and 100% in 2026, as Pensions Age reported. Jo Fellowes, who chairs the SPP’s administration committee, framed it this way: “The challenge is therefore not whether AI should be used, but how it can be used safely, transparently and with appropriate oversight.”

The Pensions Regulator reached the same place in its AI Plan of 20 May 2026, stating that trustees remain accountable for decisions and outcomes even when they delegate activities to providers or advisers. No new obligation was created. An existing one grew a much larger surface area.

The gap between a principle and a control

Here is where the paper gets uncomfortable, and where it earns the attention of anyone outside pensions.

“Meaningful human oversight” is the principle everyone signs. It is also the one almost nobody can currently evidence. The SPP invokes the Information Commissioner’s Office, whose draft guidance on automated decision-making went to consultation on 31 March, and the ICO’s test for meaningful human involvement is unusually concrete. The reviewer has to be trained to understand the system’s logic, outputs, limits, and risks. They have to hold the authority and the information to reach a different conclusion, not merely endorse the machine’s. They have to review while the decision can still be changed. And they have to do it every time, because spot checks leave everything else unchecked.

Asked by CFI.co whether current administrator and adviser practice would satisfy those criteria, the SPP declined to claim it would. Fellowes said trustees and the industry are still getting to grips with AI uses and how to govern them, so there has not been enough challenge of administrators and advisers to understand what practices they actually have in place.

That is an unusually honest answer from a trade body, and it names the real problem. The distance between writing “a human reviews the output” in a policy and being able to prove it under questioning is enormous. It is filled with people, calendars, tooling, records, and someone senior enough to say no.

Who owns this on Monday

Every organization I talk to about AI governance is somewhere on the same three-step path, whether they run a pension scheme or a 60-person manufacturer.

They write the policy. That part is fast, and increasingly there is a good published framework to start from, which is exactly what the SPP has just given the pensions industry for free.

Then they discover the policy describes an operating model nobody is operating. There is no register, because building one means asking eleven departments what they are actually using and getting honest answers. There is no risk tiering, because tiering requires someone with the standing to tell a director their favorite tool is high risk. There is no evidence trail, because nobody specified what evidence looks like before the systems went live.

Then they either staff it or they do not. Staffing it properly means a Chief AI Officer, and for most mid-market organizations that role is real but not full-time. That is the gap Modular Technology Group’s fractional CAIO practice exists to fill: an executive who owns the AI program, writes and maintains the policy, runs the AI register and the sanctioned tool list, tiers the use cases, sets the review cadence, and shows up to the board meeting with the reporting the framework asks for. An AI Program Office behind that keeps the artifacts current between meetings, because a register that is six months stale is worse than none, since it looks like control.

The framework tells you what good looks like. It does not tell you who does it, and it cannot. That part is an org chart decision, and it is the only part that changes anything.

Your data, your rules, and that includes the AI working on it

Your AI, your rules. Not the vendor’s rules, not the model provider’s terms of service, and not a control that exists in a document and nowhere else.

Ask the three questions the SPP is really asking. Where is AI being used across your operation right now, including the parts nobody approved? Who is accountable for each of those uses by name? If a regulator, a client, or your own board asked you to reconstruct why an AI-assisted decision came out the way it did, could you?

If the answers are uncomfortable, you are in normal company. The SPP just told an entire industry the same thing in writing. The organizations that come out of this well will be the ones that treated the framework as a starting position rather than a finished deliverable, and put a name next to every line of it.

The post A framework is not a control. Somebody has to run it. appeared first on Modular Technology Group.

Cale is back from Ai4 in Las Vegas, and the news wrote his trip report for him. Three stories this week, and one question underneath all of them: when an agent acts, who answers for it?

In this episode

• A federal court says the human who points the agent is the one who acted. On August 4 the Ninth Circuit vacated the injunction Amazon had won against Perplexity’s Comet shopping agent. Under the federal anti-hacking law, the court read “access” as a person entering a system; software is a tool, and tools do not carry liability. One circuit, an early stage, and Amazon’s other theories are still live. Opinion, No. 26-1444 (PDF)
• “A human reviews it” has a measured failure rate. Belgian developer Alex Wauters built a browser game that flashes real agent permission prompts on a sixty-second clock. Across more than forty thousand rounds, players approved roughly one dangerous request in three. Anthropic’s own telemetry puts real-world approval around ninety-three percent. The Register
• Half a continent could last one business day. A survey of 1,500 firms across the UK, France and Germany found 73.9% worried Washington could cut off their access to US technology, and 54.5% said they could operate for a single business day without their cloud services. 44% have a continuity plan they have tested. The survey was commissioned by Proton, which sells the sovereign alternative; weigh it accordingly. The Register

Report from the floor: Ai4 2026

Cale worked more than thirty booths at The Venetian, August 4 to 6. Three things he brought home.

• The argument won. A main-stage keynote titled “From Renting AI to Owning Intelligence.” Appliance vendors printing “No tokens. No surprises.” on their booth walls. The CIO of a NATO procurement agency describing keeping local models close for sensitive work. Owning your AI has stopped being a niche position.
• Most “agent governance” is a dashboard. It shows you what your agent did. Far fewer products will stop it, and watching an agent misbehave in real time is not a control. The crowd of new names read like the late nineties; Cale’s own bet, offered as a bet, is that many are gone within a year. If your oversight lives in a startup’s dashboard, your controls inherit that startup’s odds.
• Ng and Hinton. They disagree about where this goes, and Hinton’s doom ran a little thick for Cale. The take-home fits in two sentences: build now, the way Ng says. Govern like Hinton is right.

The question we asked

If one of your agents did something wrong yesterday, could you reconstruct what it touched, or would you be guessing? Get the Modular Briefing by email and reply to it. A person reads every reply.

A note on the voices

Laura and Arthur are AI-generated voices, produced locally on Modular’s own infrastructure. The reporting, editorial judgement and script are the work of the Modular team. Facts and figures are drawn from the linked sources; check them there before acting on anything.

Transcript

Your data, your rules.

The post The Modular Briefing, August 10, 2026: The Agent Was You appeared first on Modular Technology Group.

Three stories this week, and one question underneath all of them: when the AI does something, whose name is on it?

In this episode

• Your team is already using AI outside their job. An OpenAI analysis of more than 800,000 work messages found 44% of occupation-specific messages involved tasks outside the sender’s own role. For HR professionals it was 69%, third highest of any group, and the crossover runs highest at small companies. HR Dive
• What real oversight looks like. The Society of Pension Professionals published a five-principle AI governance framework. Two travel to any industry: tier AI uses by risk instead of writing one blanket rule, and make sure the human reviewing an automated decision has the authority to overturn it. Finextra
• “AI did it” is not a defense. After a rogue agent went after another company’s systems, lawyers were asked who is liable. Nobody knows yet, because the law was built around human decision makers. Liability turns on who designed the system, who set its objectives, what safeguards existed, and how much independence was judged acceptable. The Register

From the week

Cale’s post on a bank hiring its first Chief AI Officer reached more than thirteen thousand people. Two later versions of the same argument reached about two hundred each. The difference was posting the day the news broke and opening with the specifics.

On the road

Modular is at Ai4 in Las Vegas, August 4 to 6, at The Venetian.

The question we asked

At your firm, right now, if somebody asked who approved the AI that touches your client files, is there a name? Or is there a document? Tell us. Get the Modular Briefing by email and reply to it, and it lands with a person who reads it.

A note on the voices

Laura and Arthur are AI-generated voices, produced locally on Modular’s own infrastructure. The reporting, editorial judgement and script are the work of the Modular team. Facts and figures are drawn from the linked sources; check them there before acting on anything.

Transcript

Your data, your rules.

The post The Modular Briefing, July 31, 2026: Is There a Name on It? appeared first on Modular Technology Group.

That is not a story about lazy employees. It is a story about missing guardrails.

The real risk is ownership, not the tool

When someone submits work they do not understand, the organization has quietly taken on a liability it cannot see. A wrong figure in a board deck, an unsupported claim in a client memo, a compliance answer that sounds right and is not: each of these is now traveling under your name, and no one in the building can defend it. The tool did not create that exposure. The absence of an owner did.

This is the pattern behind most shadow AI. People adopt AI because it helps, not because they were reckless. They paste a contract into a public chatbot to summarize it. They let a model draft the analysis and move on. Every one of those choices is reasonable in isolation. Added up across a department, with no policy and no review, they become an accountability gap that widens every week.

Banning the tool is the wrong instinct

The reflex in regulated and high-stakes work is to lock AI down. Block the sites, forbid the tools, wait for the risk to pass. It never does. The work still needs doing, the tools are still faster, and the usage simply moves somewhere you cannot see. A ban does not remove the risk. It removes your visibility into it.

The better move is to govern the tool the same way you govern any other capability that touches sensitive work. Name an owner. Set the boundaries. Give people sanctioned tools that are good enough that they stop reaching for the unsanctioned ones. Your AI, your rules, the same principle you already apply to your data.

What governance looks like in practice

Governance sounds heavy. In a mid-sized company it is closer to a short list of decisions made once and enforced consistently:

• An accountable owner. Someone whose job is to say what AI is allowed to touch, and to answer for it. This is the core of the fractional Chief AI Officer role: senior judgment on AI risk without a full-time executive hire.
• Sanctioned tools with real boundaries. A private, governed workspace where your data stays inside your walls and never trains someone else’s model. When the approved option is genuinely useful, shadow AI loses its pull.
• Oversight that fits the work. Higher-stakes output gets a human check before it ships. Routine work moves fast. Match the level of review to the level of consequence, not the other way around.
• Training that closes the gap. People should be able to explain what the AI did for them. That is a skill, and it is teachable.

None of this requires banning anything. It requires deciding who is responsible before the work goes out the door, not after it comes back wrong.

The through-line

Every organization is going to run on more AI next year than it does today. The question is not whether your team uses it. They already do. The question is whether the work it produces is something you can stand behind.

That comes down to ownership. Own the tools, own the data, own the decision about who is accountable. A duty of care, not a ban.

If you are not sure who owns AI risk in your organization, that is the first thing worth fixing. It is exactly the problem a fractional Chief AI Officer is built to solve, and it is where we usually start.

Talk to us about AI governance →

Your data, your rules. Modular Technology Group helps regulated and mid-sized teams run private AI on infrastructure they own, from dirt to desktop.

The post When Your Team Ships Work They Can’t Explain appeared first on Modular Technology Group.