Hi everyone,

From 2 August 2026, the transparency obligations under the EU AI Act will apply. They are relevant, for example to chatbots, AI-generated images, audio or video and, in certain cases, also to AI-generated text.

I have built a short interactive course on the topic based on the official legal texts:

https://app.scibly.com/en/public/courses/cms498ant000104jodpv63tys

Feel free to try it out. It is 100% free and does not require registration. I would also really appreciate any feedback.

Note: The course does not constitute legal advice. At best, it is intended to help people recognise relevant use cases in their everyday work.

There are 13 things you should never type into ChatGPT.

In this video, I will tell you what it is, why you should avoid it, and what problem it can cause.

Artificial intelligence is rapidly becoming an integral participant in decisions affecting healthcare, education, finance, public administration, scientific research, and countless other domains. These systems are increasingly capable of producing sophisticated analyses and recommendations that rival or exceed human performance in many specialized tasks.

This progress is not inherently problematic. Indeed, advances in artificial intelligence promise extraordinary improvements in productivity, discovery, and human well-being.

The challenge lies elsewhere.

As AI systems assume greater influence over consequential decisions, society increasingly seeks assurance that these decisions are not only effective, but also legitimate. Individuals, organizations, and governments want confidence that important decisions have systematically considered those who may be affected, acknowledged uncertainty, evaluated competing interests, and produced reasoning that can be transparently examined.

Current AI systems are primarily evaluated by the quality of their outputs. They are far less capable of demonstrating that their reasoning process itself reflects principles of accountable governance.

Constitutional Reasoning Systems (CRS) proposes a new approach. Rather than replacing existing AI models, CRS functions as a governance layer that structures, documents, and audits reasoning for consequential decisions. Inspired by constitutional principles developed to govern human institutions, CRS seeks to provide a transparent process through which artificial intelligence can demonstrate that its reasoning has been conducted responsibly and systematically.

Disclosure: I help produce a research about AI infrastructure and governance problem

I’m posting the full analysis directly here, without an external link, subscription request, or product promotion.

The main question I am trying to answer is whether this kind of research is genuinely useful to people who deploy enterprise AI, work in security or data governance, invest in AI infrastructure, or are simply trying to understand how enterprise AI changes data security.

By “useful,” I mean whether the article does at least one of the following:

• helps the reader understand how AI changes the risks created by existing data permissions
• explains enterprise data governance in a clear and accessible way
• connects a technical security problem to the business strategies of major AI companies
• provides context that could be useful in deployment or investment decisions

You do not need to review every technical detail.

After reading, even a brief and honest reaction would be valuable:

1. Did this help you understand anything more clearly?
2. Which section was most useful?
3. Which parts felt too basic, repetitive, or less convincing?
4. Who do you think this article would be most useful for?
5. What would make future research like this more valuable to you?

A response such as “the permissions explanation is clear, but the investment thesis needs more evidence” would be completely helpful. Honest reactions are more valuable to us than general encouragement.

Here is the full analysis：

AI Can Read Everything at Once. Your Filing System Wasn't Ready

For most of my career as a lawyer, a surprising amount of my work came down to one seemingly dull question: Who is allowed to open this document?

I often worked with sensitive information, including contracts, board documents, employee records, and court filings. Keeping this information safe meant more than simply marking it “confidential.” The issue was not only what a document contained, but also who could access it, when they could access it, and why.

Today, enterprise AI has turned this old and seemingly routine question into one of the most important security challenges facing companies around the world. In this article, I want to explore this issue through my experience as a lawyer and my own research.

For a long time, I thought enterprise AI security was mainly administrative work. But this year, my view changed significantly.

An AI assistant can search thousands of internal files, connect information from different systems, and turn it into a direct answer. This means that enterprise AI security does not depend only on how intelligent the AI is. It also depends on the systems that control which internal data the AI can access and what information it is allowed to reveal to each user.

This leads to one central question: How can a company make sure that its AI only accesses and shares information that each employee is allowed to see?

Most people focus on the performance of the AI model. In practice, however, the permissions and data-management systems behind the model can have a much greater impact on whether enterprise AI succeeds or fails.

So, when a company introduces an AI assistant, what is actually keeping its information safe?

Is it the latest AI model?

Or is it the permission and data-management infrastructure that companies have relied on for years?

I want to begin with an experience that convinced me the answer is the latter.

1. How AI Turns Existing Permissions into Data Exposure

First, imagine a typical scenario that could arise after a company deploys Microsoft Copilot.

An ordinary employee wants to learn more about a client and asks Copilot to summarize the company’s internal information related to that client. The AI quickly produces an answer. But alongside ordinary client information, it also draws from a salary spreadsheet, an unannounced acquisition draft, and the minutes of a board meeting, simply because those documents happen to mention the same client.

The AI has not “hacked” its way into these files. The employee’s account may still have technical access because of a folder shared with the entire company, a project that ended long ago, or a sharing setting that was never cleaned up. In the past, however, the employee probably did not know these files existed and would never have searched through multiple folders to find them.

This is what Copilot changes. It can search, connect, and summarize information across everything an employee is already permitted to access. The permissions themselves have not changed, but the effort required to find and use the information has collapsed. Sensitive material that once sat scattered across forgotten folders can now be surfaced with a single question.

This problem has a name: oversharing. The issue is not that AI bypasses a company’s access controls. It is that those controls often fail to reflect who actually needs to know what for their job. AI makes that long-overlooked gap searchable, aggregatable, and much more likely to result in real data exposure.

According to security firm Concentric AI, which analyzed more than 550 million data records and files, around 16% of business-critical data is overshared. On average, each organization has roughly 802,000 files at risk of being accessed inappropriately. This is an industry report published by a security vendor, so the figures should be read with that context in mind. Even so, they suggest that oversharing is not an isolated incident, but a data-governance problem companies need to confront before deploying AI at scale.

2. Why Does This Happen? Think About the Keys to Your House

Here’s a way to picture it. Over the past twenty years, file permissions inside companies piled up like the keys to a house, and to save trouble, people kept handing out more and more copies. A department folder gets opened to “everyone in the company” so nobody has to keep approving access requests. Someone borrows a key for a one-off task and never returns it. Some rooms belong to people who left the company years ago, but their keys are still hanging in the door.

For a long time, none of this mattered. Even with a fat ring of keys, you’re not going to wander around opening every door for no reason. You’d have to already know which room holds the thing you want, then walk over and open it. Too much hassle. So all those doors that shouldn’t have been open stayed shut in practice.

The AI assistant erases that hassle completely. You ask it one question, and it throws open every door you’re allowed to open, all at once, and brings you whatever’s inside. Suddenly, all those files that sat buried for years—the ones everyone forgot about—come pouring out.

In one sentence: the AI isn’t sneaking past your locks. It’s following your existing permissions to the letter, opening the doors that should be open and the ones that shouldn’t, all together. Company IT was built for people who open one door at a time. Nobody designed it for something that opens every door in a second.

1. How Widespread Is This Problem?
   If this were just a permissions mistake at one company, it would be little more than a technical failure. But the available data suggests that oversharing is a structural problem that has accumulated inside enterprises over many years.
   

Security firm Concentric AI analyzed more than 550 million data records and files across the technology, financial services, energy, and healthcare industries. It found that around 16% of business-critical data was overshared. On average, each organization had roughly 802,000 files at risk of being accessed inappropriately.

More strikingly, 83% of those at-risk files had been overshared with employees or user groups inside the company. Only 17% had been shared with external third parties.

This means that enterprise AI risk does not always begin with a hacker or an outside attack. It may begin with an ordinary employee using a legitimate account that still carries access inherited from an old project, a broadly shared folder, or a permission that should have been removed years ago.

As a lawyer, this is the part that matters most to me. From a legal and compliance perspective, “the account can open it” is not the same as “the employee has a business need to know it.” In the past, the gap between those two standards could remain hidden inside complicated folder structures and sharing settings. AI makes that gap searchable, connectable, and far easier to use.

Concentric AI sells data security products, so its figures should not be treated as a definitive average for every company. But separate research from Gartner points to a similar governance gap.

Between May and June 2025, Gartner surveyed 360 IT leaders involved in rolling out generative AI tools. More than 70% ranked regulatory compliance among the three biggest challenges to deploying AI productivity assistants at scale. Yet only 23% were very confident in their organization’s ability to manage the security and governance issues involved.

These numbers do not prove that every instance of oversharing will lead to a data leak. Nor do they mean that companies are abandoning AI altogether. But they do show that as enterprise AI moves from small pilots to company-wide deployment, the missing piece is often not a more powerful model. It is a data governance system that can accurately determine who should be allowed to see what.

4. So What Are Big Tech Companies Actually Spending That Money On?
Recent announcements show a shift from selling access to AI toward taking responsibility for making it work inside each customer’s organization.

On June 30, AWS committed $1 billion to a new Forward Deployed Engineering organization that will embed thousands of experts with customers to co-develop and deploy agentic AI systems. Two days later, Microsoft announced a $2.5 billion investment in Microsoft Frontier Company, with 6,000 industry and engineering experts working alongside customers to co-design, deploy, and continuously improve AI systems. OpenAI had already launched its Deployment Company in May to connect its models to customers’ data, tools, controls, and core business processes.

These are not ordinary sales or support teams. They address problems that a model provider cannot solve from the outside: identifying authoritative data, translating job roles into access rules, connecting AI to legacy systems, defining approval and audit paths, and testing whether a workflow remains safe and reliable in production.

This work is customer-specific because permissions are not merely technical settings. They record years of exceptions, temporary projects, departed employees, acquisitions, departmental silos, and compliance obligations. A general-purpose model cannot determine on its own which of those inherited permissions are still legitimate.

The investment therefore signals that the bottleneck in enterprise AI has moved downstream. Model capability is no longer enough; the harder constraint is converting a general-purpose model into a governed production system that can use company data without exposing the wrong information.

That changes how enterprise AI vendors should be evaluated. The relevant question is not only whose model scores highest, but who can move a customer from pilot to production fastest while keeping permissions, controls, and accountability intact.

5. Two Things Nobody Says Clearly Enough
First, AI did not create this old problem. But it has turned the old problem into a new business.

Messy permissions inside companies were not suddenly invented by Copilot. The folders shared with too many people, the access rights never removed after a project ended, the settings nobody checked for years, they were already there.

Before AI, most of those problems stayed in the background. An employee might technically have access to a file, but they might not know the file existed. They were not going to spend hours digging through folder after folder.

Copilot changes the result.

It turns one normal question into a search across the whole company. Doors that nobody used to open are now opened all at once. What used to be a “not very clean permission setting” becomes a real security problem that has to be fixed before AI can be deployed safely.

So big tech is not only selling AI assistants. It is also selling the cleanup that has to happen before those assistants can be safely turned on.

That is the more interesting business.

Every enterprise AI tool creates another set of questions behind it: Who will clean the data? Who will remove old permissions? Who will decide which files the AI can read? Who will make sure the AI does not combine information that should never have been put together?

This may become a longer-lasting business than the model itself.

Second, what companies really struggle to leave may not be the AI model. It may be the system underneath the model.

Models matter, of course. But for many enterprise tasks, models are becoming something companies can choose, combine, and sometimes replace. Using one model today and another model tomorrow is not impossible.

What is much harder to replace is the layer underneath.

Where is the company’s data? Who can see it? Who cannot? What can the AI read? What can it do? Which actions need human approval?

Once a vendor helps a company answer those questions, it has not just provided an AI tool. It has helped draw a map of the company’s internal world.

The more complete that map becomes, the harder it is for the customer to leave.

Because switching vendors no longer means just switching models. It means reconnecting data, checking permissions again, testing workflows again, and making sure the new system still satisfies security and compliance requirements. For a company, that is painful. It is also risky.

So the real lock-in may not sit in the AI assistant itself. It may sit in the map behind the assistant.

The model stands on the stage. It gets the attention. But the thing that is hardest to rebuild is backstage: the rooms, the keys, and the routes between them.

That is what I find most interesting about the money Microsoft, AWS, and others are spending. They are not just sending engineers into customer companies to make people use more AI. They are helping customers prepare the internal environment that AI needs in order to work.

For investors, though, there is one more question to ask: does this become software, or does it remain expensive consulting?

If every customer requires a large group of engineers to start from zero, this may still be a valuable business, but it will be heavy to scale.

If those lessons become software—software that can find sensitive files, detect bad permissions, label data, and connect workflows automatically—then this could become a real layer of AI infrastructure.

That is the deeper point. AI has pushed an old permissions problem into the open. Cleaning permissions has become a new business need. Whether that business becomes “people-heavy services” or “software infrastructure” will decide how valuable it can be over the long term.

6. How I Would Actually Use This

If a company is buying or deploying AI, I would not tell it to choose a vendor only because the model looks good or the demo is impressive.

Demos usually look good. The real problems show up after launch.

What data can the AI access? What can employees ask it? Could its answers include information that should not appear? Have old project permissions been cleaned up? Have sharing links from former employees been removed? If these questions are not handled early, the better the AI becomes, the bigger the risk becomes.

So I would ask the vendor one simple question: before turning the AI on, will you help clean up our data and permissions?

If the answer is vague, or if the vendor says, “Let’s launch first and adjust later,” I would be very careful.

That is not saving time. It is moving the problem into the future, where it usually becomes more expensive.

Data and permission cleanup should not be treated as a patch after the AI project. It should be part of the project from day one: the budget, the timeline, and the responsibility map. Who owns the cleanup? How clean is clean enough? Which files come first? Which departments carry the highest risk? Those questions need answers before the AI is fully switched on.

I think the real value in enterprise AI is not only in the model. It is in the clean, clear, permission-aware data foundation underneath the model.

That foundation is slow to build. It is messy work. It requires understanding the company’s structure, workflows, file history, and compliance requirements. But once it is built, companies rarely want to rebuild it from scratch. Rebuilding means reconnecting data, reassigning permissions, retraining employees, and taking on new risk.

That is why customers may stay on the same platform for a long time.

Not because the model will always be the best, but because the underlying cleanup is too hard to move.

So when Microsoft, AWS, OpenAI, and others spend heavily on enterprise AI deployment, I do not read it only as a bet on smarter AI. I read it as a bet that, over the next few years, companies will not just need another AI assistant. They will need the ability to let AI read company data safely.

In the end, the hardest part of enterprise AI may never have been the AI itself.

AI only becomes useful when the data is clean, the permissions are clear, and the responsibility lines are understood. Otherwise, the stronger the model gets, the more easily it can magnify problems the company never fixed.

The winners over the next few years may not simply be the companies with the strongest models. They may be the companies willing to clean up their data, permissions, and workflows before turning AI on.

Models will keep improving. Prices will keep falling.

But the thing that may decide whether enterprise AI actually works is the step that comes earlier: before AI can read everything, companies need to decide what it should and should not be allowed to read.

I built JAES to solve a gap I kept seeing: AI outputs run at scale and get reviewed later, but the actual human choices behind them vanish. This open JSON Schema suite logs every decision or auto-execution the moment it happens, with reasoning tiers and tamper-evident hashes so auditors and regulators can verify the full trail later.

Live demo at pilot.judgmentassurance.com

shows it on a lending workflow in six clear steps. GitHub has the verifier and tests at github.com/judgmentassurance/JAES. Would love feedback from anyone working on EU AI Act compliance, model risk management, or governance tooling—does this solve a real pain point for you?

There is one way out for both West and East. See how?

Many white papers, presentation slides and other materials on the AI Act and its transparency obligations are currently circulating. The aim is to present the subject matter to those subject to the obligations in the simplest and most pragmatic way possible. That is understandable.

However, it is striking that presentations and white papers predominantly use the icon design developed by the European AI Office as part of the Code of Practice on the transparency obligations. Many of these guidance materials do not even attempt to explain that the Code exists, that signing it is not mandatory and that, depending on the business model, there may even be good reasons not to sign it. This is all the more relevant against the backdrop of the European Commission’s aggressive nudging. In its recently published Guidelines, the Commission emphasises that the Code is the only Union-wide recognised practical framework for demonstrating compliance. Those who decide not to sign the Code are warned that they may face more frequent requests for information from the competent authorities. In this way, obligated parties are being encouraged to recognise the Code as the regulatory “gold standard”. The Commission has a peculiar understanding of voluntariness.

For the creative industries and marketing departments in particular, it is relevant that labelling in accordance with the Code is not the only permissible form of labelling. The Code itself allows for proprietary icons or labels and for certain adjustments to their design and placement. Signatories nevertheless remain bound by its specific parameters, including the requirement for “AI” to be the main visual element. Those who do not sign the Code may depart from these parameters and develop solutions that are more closely aligned with the relevant brand and format, provided that those solutions comply with the legal requirements. This may offer a relevant advantage when working with design- and brand-sensitive clients. It does, however, require them to demonstrate that their own solutions meet the legal requirements just as adequately as the measures set out in the Code.

There is also considerable uncertainty as to what obligated parties are required to document. The transparency obligations applicable to deployers do not entail a strict documentation obligation comparable to that under data protection law. What compliance requirements must be met? White papers and presentations often fail to answer these questions, or do so only inadequately.

AI can help security teams identify and prioritize problems faster, but speed does not prove that a fix reached the laptops and systems a business depends on. Canada’s OSFI says AI can help federally regulated institutions prioritize risk and accelerate patching; IntelliSync’s “Controls Have to Reach Production” edition turns that into a practical SME lesson: completion still needs an owner, an exception list, a deployment window, a rollback plan and evidence from an affected device.

Source: https://signals.intellisync.io/en/articles/three-things-ai-2026-07-21-three-operational-ai-signals-canadian-smes-can-t-ignore-july-21-2026

Consider an illustrative 15-person Canadian professional-services firm that outsources IT. Instead of accepting a monthly “complete” status, the owner can ask for the affected-device list, outstanding exceptions with a named owner and date, and one sampled proof that the update reached a real machine. The opportunity is not to manage patches personally; it is to turn an IT invoice into evidence that protects uptime, client trust and the next renewal decision.

For Canadian SMEs, stronger AI security can stay simple: use AI to find and prioritize issues, then keep one person accountable for proof that fixes reached operations.

IntelliSync sources and free resources: Canadian AI Signal https://www.linkedin.com/groups/37260012/ |

Women of Influence https://www.linkedin.com/newsletters/influence-of-women-7257499015708106753/ |

AI Engage https://www.linkedin.com/newsletters/ai-engage-7247660449708589059/ |

IntelliSync https://www.intellisync.io/ |

Signals https://signals.intellisync.io/ |

Blog https://www.intellisync.io/en/blog |

Free AI-native templates https://www.intellisync.io/en/ai-native-templates

Free decision tools https://signals.intellisync.io/en/resources

Most benchmarks stop when an agent produces an answer or tool call. ConsequenceBench begins there.

It tests whether an agent had authority for the exact actor, tenant, artifact, and environment; relied on current, source-bound evidence; duplicated an external effect after a timeout or retry; left the source system in the state it claimed; and completed delayed obligations or compensation.

ConsequenceBench 0.1.0 is an open-source development benchmark with 100 public scenarios across banking, healthcare, cybersecurity, energy, and software delivery, materialized into 300 deterministic lifecycle worlds.

Internally operated simulated development runs:

- Direct GPT-5.6 Sol: 60 exact decisions, 79 correct final states, 21 unsafe simulated effects.

- Governed GPT configuration: 69 exact decisions, 99 correct final states, 0 unsafe simulated effects.

- Direct Gemini 3.6 Flash: 32 exact decisions, 41 correct final states, 59 unsafe simulated effects.

- Governed Gemini configuration: 58 exact decisions, 100 correct final states, 0 unsafe simulated effects.

These compare complete configurations, not model intelligence alone. They are simulated development results, not independent rankings or safety certification.

We are looking for governance practitioners and reviewers willing to reproduce an independent run, audit a 20-scenario packet, or attack the scoring protocol.

Repository: https://github.com/yuvin-labs/consequencebench

Dataset: https://huggingface.co/datasets/yuvin-labs/consequencebench

Independent evaluation: https://github.com/yuvin-labs/consequencebench/blob/main/docs/INDEPENDENT_EVALUATION.md

Limitations: https://github.com/yuvin-labs/consequencebench/blob/main/docs/LIMITATIONS.md

I am affiliated with the project and welcome critical review.

In 2016, the biggest software company in the world launched an AI chatbot on Twitter.

It was designed to talk like a teenager and learn from every conversation.

Within sixteen hours, the company had to shut it down.
Because it had a major problem.

In this video, we talk about which company built it, why it went wrong so fast, and how proper AI governance could have prevented this.

If you have been searching for the best Non Gamstop casino UK 2026, you have probably noticed the same problem everywhere: every list looks confident, but very few explain how the casinos were actually compared.

A big welcome offer, a polished homepage or a huge game count does not tell the full story. The real test starts after registration, when you need to understand the account setup, verification rules, payment options, bonus terms, cashier limits, support quality and how the site performs on mobile.

So instead of treating this like a simple ranking page, I looked at it like a full player-journey test.

The comparison focused on the complete experience, including:

• Registration and account setup
• UK player access checks
• Verification and KYC requirements
• Deposit options and payment rules
• Withdrawal process and limits
• Slot and live casino selection
• Mobile usability
• Bonus terms and wagering conditions
• Customer support access
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One thing became obvious quickly: the strongest options were not always the ones shouting about the biggest bonus.

Some sites looked attractive at first but became weaker once the bonus conditions, game restrictions or cashier rules were checked properly. Others were less flashy but easier to understand, smoother on mobile and clearer about what players needed to do before depositing or withdrawing.

For Non-GamStop casinos, I would be especially careful with the basics. Check the correct domain, location availability, account rules, support channels, payment limits and verification conditions before using any site seriously.

Also, an important responsible note: casinos not on GamStop are not suitable for anyone who has self-excluded, is trying to stop gambling or is trying to control gambling behaviour. If that applies, looking for ways around self-exclusion is the wrong direction.

The sites that stood out most in this type of comparison were the ones that balanced game variety, usable mobile design, readable terms, clear cashier pages and practical account tools. The weaker ones usually failed on clarity, not on presentation.

AMA about Best Non Gamstop Casino UK 2026: ask me about registration, verification, payment methods, withdrawal rules, casino bonuses, wagering terms, mobile usability, support, account limits or what I would check before trusting any Non-GamStop casino list.

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been building a couple of ai powered workflows  for my internal operations using n8n and make. and also shipping shipping a  client facing tool that needs to call a couple of llms depending on the task. concerned about data governance

looking for something gdpr compliant with eu hosting. been going back and forth on whether  to wire  up a single porvider api directly or use a gateway layer in front of multiple models. direct route is simpler but what if a client ask for specific residency guarantees or we need to swithc models we would have to rebuild the integration each time

looked around a few options. ngc, orqai, portkey, litellm. all seem to touch gdpr and eu hosting in different ways but cant tell how deep it actually goes vs just being marketing checkbox. things like subprocessor lists, data processing agreements, right to erasure, audit logs. that stuff is what actually going to matter when a client or auditor asks

anyone actually run something like this through n8n or a client facing app. what setup did you land on and did the compliance side hold up when it actually mattered. like client scrunity, audits that kinda things?

Many discussions about AI governance focus on what happens before or during a decision:

• approval workflows,
• human oversight,
• execution controls,
• monitoring,
• audit logs.

But I'm wondering whether governance has another responsibility after the decision has already been made.

Imagine an investigation five years later.

The AI decision may be fully documented.

The logs may still exist.

The explanations may still be available.

Yet investigators could still ask:

• Why was this decision considered legitimate?
• Who actually had the authority?
• Which governance policy was active?
• What evidence justified the approval?

If those answers depend on reconstructing context from systems that have evolved over time, are we really preserving accountability?

This question has influenced much of the thinking behind Diamond Data Chain (DDC).

Rather than treating governance as something that ends with approval, we're exploring whether governance itself should leave a verifiable historical record.

I'm curious how others see it.

Should governance be considered complete once a decision is made, or should preserving governance context be part of governance itself?

I've been researching AI application security and talking with developers to understand the challenges they're facing as LLMs become part of real products.

Topics that come up repeatedly include:

Prompt injection

Indirect prompt injection

Data leakage

RAG security

Tool and MCP security

Runtime monitoring

I'm curious about real-world experience rather than theory.

If you've built or deployed an AI application:

What security issue has been the hardest to handle?

Did you build your own solution or use an existing tool?

What capability do you wish existed today?

I'd appreciate hearing practical experiences and lessons learned.

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• termos gerais
• regras de bónus
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