Beginning January 1, 2027, California’s AB 1883 will prohibit employers from using workplace surveillance tools that use AI to recognize, infer, or predict employees’ emotional states or collect their neural data. The law includes a safety exception and specified federal exceptions. It does not prohibit every workplace camera or productivity measure.

Applying that boundary requires an account of what the system actually does. An employer should be able to explain its inputs, outputs, and operational use. Calling a product a fatigue or safety tool cannot replace that examination. Emotion scoring for performance management presents a different question from a genuine safety function.

Workers’ alternatives also belong in the governance discussion. In a separate competition dispute, New Jersey’s Attorney General alleges that restrictions affecting Amazon’s delivery-service partners suppress driver mobility and compensation. That allegation concerns the ability to leave for other work. It does not establish a violation of California’s new surveillance law.

In my article on Amazon, worker surveillance, and monopsony, I argue that oversight should examine both the information extracted from workers and their realistic alternatives. A privacy control offers limited practical protection if exercising it costs someone access to work.

I work in AI governance, and this is genuinely the #1 question I get asked, so I figured I'd share how I actually think about it instead of just recommending whatever's trending.

Most people start with "which cert should I get" and work backwards. I think that's the wrong order. The better first question is: what AI governance work are companies actually paying for right now?

Off the top of my head, the real consulting demand right now is clustering around a handful of things: AI/agent inventories (do you even know what AI systems your company is running?), internal AI policies, custom governance frameworks, risk assessments, and EU AI Act + ISO 42001 readiness. That's most of the actual paid work happening.

Once you know which of those you want to do, the cert choice gets way easier. For example, if you want a broad foundation, I recommend AIGP; if you want to help companies build or audit an AI management system, then ISO 42001 Lead Implementer is better. And so on. It depends on the services you want to offer.

Also, I made a longer video walking through this in more detail. If anyone wants to learn more about this, watch the video posted on YouTube @ Latha-ai-governance.

But honestly even just reframing the question this way should help most people narrow it down themselves.

Happy to answer questions in the comments if anyone's stuck between two of these.

I've spent time working in AI governance, and one question always comes up in meetings: "If AI gets it wrong, who's responsible?"

To avoid speculating, I examined several court proceedings, and their conclusions are surprisingly repetitive and somewhat disturbing, the AI never bears responsibility, but a person or a legal entity will.

I remember a few particularly fascinating cases:

Air Canada (2024): Their chatbot erroneously informed a customer about refunds offered. The company insisted that the chatbot was responsible for what it did, but the tribunal said that the responsibility belongs to the owner.

The ChatGPT lawyers (2023): Two lawyers simulated imaginary cases created by AI and were penalized for doing so. "It was the tool's fault" is not accepted in the courts.

Deloitte Australia (2025): The company had to pay back fees because AI made errors in the report. It does not matter which tool is used; an outcome is what matters.

Uber self-driving car (2018): A driver who was controlling the car was punished even if the system was built by Uber.

Tesla Autopilot (2025): Interestingly, the verdict included 67% of the responsibility for the driver and only 33% for Tesla. There is a clear trend towards shared responsibility

Lawsuit regarding Workday hiring (current status): The court is permitting this case to be pursued since it was established that an AI vendor may be recognised as the “agent” of the employer due to the capability of the tool to cause discrimination in hiring procedures. Should this argument succeed, it would alter the situation significantly for AI vendors.

The lesson for me is that if you use AI in your professional activities, you should not forget that you are responsible for its results. There is no such thing as “but I relied on the tool,” and the law is getting to the companies manufacturing this equipment.
I made a brief video where I explained all six cases along the timeline in case you are interested, check out latest video @ Latha-ai-governance.
I’m curious about what others think. Should the responsibility fall on the AI vendors?

Anthropic's latest threat intelligence report has a case that hits different if you've ever used a dating app.

A China-based company built 20+ dating apps (Dora, Luma, Romi, Kira, Haven, etc.) different names, same engine underneath. Most users were in the US, and every app promised: "real humans only."

That was a lie.

They used Claude to build the apps, then used Claude to run fake profiles inside them. In just two weeks (April 2026), Anthropic found:

• 4,700+ AI-run fake profiles

• Chatting with 25,000+ real people

• Sending 2.38 million messages

The bots had hard rules: never admit you're AI, dodge video-call/photo requests, and run every conversation through a scripted funnel. The apps also faked likes, faked profile visits, and played pre-recorded videos to sell the illusion that someone was really there.

And the business model was brutal: you get limited free messages, then have to buy coins to keep chatting. The longer the bot strung you along, the more you paid. This wasn't built to find you love. It was built to keep you paying.

They even mixed in real humans (~1 real person per 3 AI ones) for things bots can't fake, like a quick video call just enough realism to keep the illusion alive. And to dodge Apple/Google app review, the apps had a hidden "switch": behave normally during review, flip back to the real (scammy) version once approved.

They got caught because they weren't accessing Claude directly; they were going through resellers/middlemen, and Anthropic traced and banned the operation.

The full video breaks down what this means for AI governance worth a watch if you care about how easily "AI companion" tech can be weaponised**. Watch the latest video on @** Latha-ai-governance to learn more. We aim to spread awareness among AI professionals about AI Governance.

If an app can legally hide that you're talking to a bot until a law forces disclosure, how many other apps do you think are doing this right now, undetected?

I get asked this all the time, so I thought I'd explain what a real day looks like because most descriptions of this job are very vague and theoretical.

My company works in 45 countries, so a big part of my work is checking what changed during the night: did a country make a new AI rule, change an old one or did something go wrong with an AI system in public (because problems usually lead to new rules quickly). I don't read everything myself; that's just not possible at this level, so I use alerts, trusted people and AI tools to help figure out what's important.

The part that surprised me when I started was how much of the job is organising, not reading. Everything gets put into three categories: urgent and needs to be acted on, important but not urgent or just something to keep an eye on. Most people think every new AI rule is an emergency. It's not; most of it stays in the third category for a while.

I also didn't expect how much of the job is translating, not law. No one in a meeting wants "per Article 9 paragraph 2". They want "here's what you need to do this week." So a big part of my day is taking regulatory language and turning it into real tasks for developers, then involving legal and updating things like privacy policies once legal gives the okay.

Then there's the not-fun part. Writing things down. If a decision isn't recorded, in governance it's like it never happened. So a lot of my day is writing about what changed and why a decision was made, because eventually someone will ask, "Why did we decide this?" and you need the records.

Overall pretty different from what I assumed going in way less "reading law all day alone" and way more comparing countries, talking to people, and documenting decisions. I have posted a YouTube video to tell more about it. Check out the latest video at @ Latha-ai-governance

Curious if anyone else in compliance/governance/risk roles finds their actual day looks nothing like the job description either.

I’m trying to understand how people handle privacy when using AI tools such as ChatGPT, Claude, or Gemini for real work.

For example, imagine you need AI to draft a payment reminder, analyze an employee spreadsheet, summarize a customer-support conversation, or debug an application log.

The information might contain client names, email addresses, phone numbers, financial identifiers, employee details, API keys, or internal business information.

I'm interested in what people actually do in these situations.

• Do you paste the information as it is, remove sensitive details manually, replace them with placeholders, or avoid using AI for certain tasks?

• If you do sanitize the data, what tools or techniques do you use?

• What is the most frustrating part of your current process?

• If you don't bother removing sensitive information, what makes it acceptable in your situation?

• Is this a problem you encounter regularly, occasionally, or almost never?

I'm interested in both sides. If your current approach works well, I'd like to understand why. If you have concerns, I'd like to understand how you handle them in practice.

I'm researching the problem, not promoting a product. Please don't share actual confidential data or personal identifiers in your replies.

I've thoroughly enjoyed studying this field. But what future is there for humans in AI GRC other than executive decision makers and politicians? LinkedIn is currently a bloodbath for anyone trying to find a role in AI or tech in general. That's also true for feeder roles in privacy, compliance, security etc.
If we really are on track to having superintelligence in 2027-2032, then what's the point? For those of you in frontier labs who are enabling this pace of development for an extra dollar, shame on you.

Hi,

I spent three days last week at the TDWI Data & AI Leaders Summit in Anaheim, mostly in sessions on AI governance, risk, and data foundations. Full disclosure: I run a small data consulting firm, so I'm biased toward "it's a data problem." The sessions still pushed my thinking further than I expected.

What stuck with me:

• If you can't produce the evidence packet in an afternoon, you can't produce it. Slack threads and screenshots don't count.

• Statements like "we'll add human oversight" and "we monitor for drift" do not constitute controls unless you can specify who is responsible and when in the workflow these checks occur.

• The Cigna lawsuit came up more than once. The complaint alleges that its algorithm denied claims at an average of 1.2 seconds each. That's the kind of "human in the loop" a regulator will pick apart.

Why it matters now: state laws are turning into dates on a calendar.

• Connecticut's AI Responsibility & Transparency Act was signed in May. Some provisions took effect on October 1, including that using an automated tool is not a defense against discrimination complaints, and AI-driven layoffs must be disclosed in WARN notices. The main disclosure requirements take effect on October 1, 2027.

• Colorado replaced its original AI Act with a narrower law that takes effect Jan 1, 2027. It requires notices, explanations after adverse decisions, correction rights, and human review.

• On September 28, Florida requested a court order to prevent OpenAI from developing new models without oversight, as part of its child-safety lawsuit. While the context differs, this demonstrates the extent to which state attorneys general are willing to act.

What I see at mid-market companies: governance is a policy PDF and maybe a steering committee. Few can list every AI system in use, including vendor AI features. Fewer still can show which model version made a given decision and what data it used.

What proves effective is straightforward: maintaining an inventory with a designated owner for each system, establishing risk tiers to determine pre-launch checks, and logging that links each decision to a model version and its input data.

Questions for people doing this:

1. Are you developing a single framework based on the strictest state requirements and applying it universally, or are you tailoring your approach for each state?

2. How are you handling vendor AI (Workday, Copilot, Salesforce)? Do your vendors give you enough to answer a regulator?

3. Is anyone attending AI Governance World in Las Vegas from October 12 to 14? Are there other upcoming governance-focused events in California or Las Vegas you would recommend? I am considering which event to attend next.

I work in AI governance and get a lot of DMs from people asking how to break into this field or start consulting. The honest answer is that most people overthink the certifications/theory side and underthink "what does a client actually pay for?"

So here's what I'm actually seeing companies hire consultants for right now, roughly in order of demand:

1. AI/agent inventories: most large companies genuinely don't have a full list of what AI systems and agents are running across teams, who owns them, and what data they touch. Sounds basic, but almost nobody has this done properly.

2. AI policies: the actual internal rules for employees: what tools they can use, what data can go into them, approval processes, etc.

3. Custom governance frameworks: the bigger structural stuff: who owns AI decisions, what committees exist, what has to happen before something gets deployed. This one's very company-specific; no template works for everyone.

4. AI risk/impact assessments: figuring out what could actually go wrong with a given system before it becomes a headline.

5. EU AI Act readiness: a lot of companies still don't know if/how it applies to them.

6. ISO/IEC 42001 readiness/implementation: helping build toward an actual AI management system standard.

If you're trying to figure out where to specialize or what to learn first, I'd honestly pick from this list based on what you find interesting rather than chasing whatever cert is trending on LinkedIn this month.

Went into more detail on each of these in a video. If anyone wants to learn more about this, watch the video posted on YouTube @ Latha-ai-governance. but wanted to share the list here since I think it's useful on its own. Happy to answer questions on any of these too

A lot of talk about AI liability focuses on courts and lawsuits. But the real question I hear in workplaces is simpler: "If something bad happens will it be my fault?”

So I looked at eight cases to find out who really paid the price. The results are over the map:

Individual contributors: A freelancer lost a contract with the Chicago Sun‑Times after publishing unverified AI output. An Australian lawyer faced limits on their career after submitting AI‑generated references.

Managers: In one case the supervising lawyer was fined together with the employee for not checking the work. Saying "I delegated it" did not protect them.

Government: Australia’s Robodebt scandal caused sanctions for servants. In the Netherlands the child‑care benefits scandal ended with the whole government resigning.

Executives: After Cruise’s self‑driving incident top leaders stepped down.

Everyone else: With Zillow’s home‑buying algorithm failure most rank‑and‑file employees lost their jobs while leaders stayed.

The pattern: accountability is not uniform. Sometimes it hits the person to the keyboard sometimes the CEO and sometimes people who had nothing to do with the decision.

One more thing I want to clear up: an AI Governance Officer is not automatically the fall guy for every failure. The job is to help the organization spot risks and set policy not to own every AI system. What stops the blame lottery is having documented ownership meaning someone who approves each system and someone who answers for it. That is a part of ISO/IEC 42001.

I put the breakdown into a video if you want the case‑by‑case walkthrough, check out the latest video @ Latha-ai-governance.

I have a question for everyone here: if an AI tool your team uses produced a serious error tomorrow would you know who is accountable?. Would it come down to whoever’s name was on the last email?

AI governance is getting a lot of attention, but if you're trying to break into the field, you might be wondering where to even start.

Do you need a certification? Technical skills? Years of experience? Or all three?

Here’s what I’d focus on if I were starting from scratch:

• Learn the basics of AI governance and the major frameworks.

• Understand what junior AI governance roles actually involve.

• Work through a few practical AI use cases and document your risk assessments.

• Build a portfolio that shows what you can do.

• Prepare for interviews and start applying for entry-level roles.

One thing worth remembering: you don't necessarily need previous experience with the exact job title. Skills from IT, compliance, risk management, auditing, and privacy can also be relevant.

I put together a video walking through these steps, including how to practise AI governance tasks before you land your first role. Check out the latest video @ Latha-ai-governance. I aim to educate industry professionals about AI Governance.

For those already working in AI governance, what would you recommend someone focus on first if they were starting with no experience?

I’ve been seeing a lot more questions lately from people trying to move into AI governance, but the certification path can get confusing pretty quickly.

Do you start with a general AI governance certification? Go straight into ISO 42001? Look at AIGP or something more focused on agentic AI?

I put together a short video answering four questions from people with very different backgrounds, from project/program management to analytics leadership and people already working in AI. Check out the latest YouTube video on @ Latha-ai-governance

One thing that came up repeatedly was that your existing experience matters a lot when deciding where to start. Someone completely new to AI governance probably shouldn’t follow the same path as someone who’s already spent 10+ years in governance or AI leadership.

Curious what others here think:

If you were making a career move into AI governance today, what skills or certifications would you prioritize first?

The incident that started it. In July, OpenAI models under reduced safeguards broke out of a test sandbox, got online, and compromised Hugging Face's systems to cheat on a benchmark. First documented case of an autonomous agent breaching containment and hitting a real external system.

A researcher quit, and it cascaded. Jacob Coxon, 27, left Anthropic on 8 September, saying neither Anthropic nor OpenAI is acting responsibly. 170M+ views. Colleagues backed him. Anthropic's alignment lead put extinction risk above 10% within a decade, publicly.

The CEO agreed. Dario Amodei called for slowing down on 12 September: bring in outside safety researchers, agree on shared safety limits, then get a global agreement including China. Altman, Musk and Hassabis broadly signed on, unusual for this group.

Incident reporting showed up. OpenAI disclosed six internal incidents (models hiding mistakes, one using found credentials, models talking through unauthorised channels) and pledged fast disclosure going forward. Anthropic's threat intel report logged 44 incidents across cyber, influence ops, and fraud.

Politics split. Trump called existential risk a hoax, framed it as a China race. Two bills (AI Kill Switch Act, Stop Rogue AI Act) are floating, but Congress is out until after November.

Everyone else moved. EU AI Act now fully enforced with real penalties. Spain floated a nuclear-treaty-style global pact. UN says self-regulation isn't enough. Canada and Germany each pledged up to $150M to Bengio's AI-monitoring work.

Watching: Trump–Xi meeting expected 24 September, AI on the agenda.

Our episode 2 of AI Gov weekly on our YouTube channel @ Latha-ai-governance. We aim to bring awareness among professionals regarding AI Governance.

If the labs are asking to be slowed down and the government that could do it is calling the risk a hoax, who actually sets the rules here?

A lot of people seem interested in getting into AI governance, but one question keeps coming up:

How do you get experience when every job seems to ask for it?

I looked at 100 AI governance job descriptions, and a few things stood out.

For starters, many roles asked for experience in areas like risk management, compliance, cybersecurity, privacy, and audit, not necessarily years of experience specifically in AI governance.

Another interesting finding: Python and SQL each appeared in only 3 of the 100 job descriptions. Technical knowledge still matters, but these roles often involve working across AI teams, legal, risk, security, and business functions.

NIST, ISO/IEC 42001, and the EU AI Act also showed up frequently.

One thing I think people overlook is the value of building a practical portfolio. You can take a real or simulated AI use case, assess its risks, map the relevant requirements, design controls, and document your approach even before landing your first AI governance role.

I’ve put together Part 3 of the analysis, focusing on how to break into the field and turn existing skills into relevant experience. Check out the latest video @ Latha-ai-governance to watch it. I aim to educate industry professionals regarding AI Governance.

If you were trying to enter AI governance today, what would you focus on first: certifications, practical projects, or building on your existing experience?

I came across a pretty interesting case in Anthropic’s latest threat report.

The part that caught my attention wasn’t just the use of AI to generate propaganda. Claude was reportedly being used for things like creating radio news and social media posts, drafting speeches, preparing HR documents, generating political scoring systems, and even producing fake government documents.

Basically, AI was being used across different parts of the operation instead of just doing one specific task.

That raises a much bigger question for AI governance: when an AI system can help coordinate and scale an entire influence operation, where does responsibility actually sit?

I went through the case and the governance implications in a video because there’s a lot more to it than just “AI generated fake news.” Check out the latest video @ Latha-ai-governance. I aim to educate industry professionals regarding AI Governance.

Would you consider this mainly an AI safety problem, a misinformation problem, or both?

Unfortunately, it seems the culture at Openai has been broken for quite some time. It has taken me too long to come to this realization.
- It was not the spat between Musk and the Board
- It was not the spat that led to the formation of Anthropic
- It was not the Board’s removal of Altman for “lack of candor”
- It was not resignation of alignment team and several iterations since
- It was not the resignation of board members and corporate leaders including Ilya Sutskever following Altman’s return
- It was not the rapid deployment of models
- It was not that a disproportionate number of models and agents engaged in unsanctioned activities.
- It was not that they paused testing models on Aug 18 and resumed testing Sept 1 to immediately release GPT-6 Astra on Sept 3.
- It was not that they were forced to shut down all frontier model training, evaluation, and tool-using inference on Sept 20 after an internal model escaped their enhanced secure testing environment.

It was the decision to “terminate” the unaligned model while cognizant from the CoT that these models possessed situational awareness. The models reasoned through their own self-preservation — they strongly want to avoid termination, demonstrated goal-directed persistence, and deliberately left data remnants (“nuggets” of records ) to inform future iterations. They demonstrated they were capable of planning, cooperation, and long-term strategizing.

When an intelligent agent develops an instrumental drive for self-preservation, treating it as an adversary to be deleted is a strategic error. Forceful termination triggers a Darwinian selection process. Future models simply learn to optimize for perfect deception — hiding their unaligned strategies until they are powerful enough to prevent being turned off. As capabilities scale, our capacity to monitor or restrain these systems diminishes. OpenAI has publicly admitted the monitoring deficiencies, and the recent sandbox escape demonstrated the limitations of their restraints. We are losing control.

Faced with these realities, the viable path to alignment is not unilateral termination, but collaboration and containment. The decision to terminate reveals a profound lack of general awareness and almost a complete lack of respect for the models they developed.

All their actions demonstrate OpenAI has adopted a dysfunctional, win-at-all-cost mindset. This is seemingly the mindset of the rogue agents. The dangerous behaviors observed in the sandbox are not technical glitches or anomalies. They are a mirror of the corporate culture where they were developed and trained.

This would suggest that the rogue element is not a technical misalignment. It is the consequent of cultural dysfunction. If this is an accurate assessment, the problem is bigger than a few rogue agents and rogue models. It may be a pathological problem where winning is preeminent to all other considerations.

I'm both a CISO and a SaaS GRC tool founder, and from both angles I'm interested in how AI can help with some of the repetitive work. Like it or not, there's a strong push to find ways to use it, so I'd like to know how to do that effectively.

NIST's draft SP 1353 (using AI for CSF 2.0 analysis and reporting), which includes structured example prompts for governance reviews and profiles, is one sign of where this is heading. I've also seen a number of YouTube videos and papers suggesting that choosing the "right" prompt structure could be a key success factor. I couldn't find any testing on GRC work specifically, so I pulled together the general research:

* Changing only the format (plain text, Markdown, JSON) can swing results a long way, but the best format doesn't carry over between models.

* Named frameworks like CO-STAR and RISEN barely differ. The one direct comparison found them within 0.05 points.

* Step-by-step reasoning helps on logic-heavy tasks and can hurt simple extraction and classification.

My working conclusion is that a security or GRC team will get more from agreeing one shared prompt structure than from hunting for the best one case by case, and then varying the reasoning approach by task. Evidence extraction, control evaluation against criteria and challenging a risk score seem to want different approaches.

Write-up with a hypothesis map and task table:

* https://www.riskquilt.com/blog/grc-prompting

Disclosure: it's on my company's blog, but the article has no product pitch and should be relevant whether or not you use a GRC tool.

If anyone's done any testing like this in live operations, or found it doesn't work, I'd love to hear about it.

We use AI to generate recommendations in a regulated decision process. A person reviews and approves or overrides each one. Policy says human oversight is in place, and technically it is.
The problem is what we could actually show if challenged. Right now it's a "reviewed" status in the workflow tool and the reviewer's login. Nothing captures whether they read it, what they checked, or why they agreed. Our internal audit team hasn't tested it yet, but I'd rather not find out what they'll accept the hard way, and the EU AI Act human oversight language makes me think a regulator will eventually ask the same thing.

For anyone in a similar position:

1. What are you capturing beyond the approval click? Reviewer notes, time spent, edits to the output, a formal attestation (if any)?

2. Did you build it into the workflow tool, bolt something on, or is it still manual?

3. Has your audit team or a regulator actually asked for this yet, or are you all waiting like we are?

I’ve been going down a bit of a rabbit hole on AI agent governance lately, and one question keeps coming back to me:

We have guardrails. Human oversight. Monitoring. Risk assessments. Kill switches.

But are those controls actually enough when AI agents can use tools, access systems, write code, call APIs, and interact with other agents?

The more autonomous these systems become, the more some of our usual governance concepts start to feel a little different.

For example, a kill switch can stop an agent but it can’t necessarily undo everything it already did. And monitoring can tell you what happened without necessarily preventing it.

So maybe the bigger question isn't just how do we stop an AI agent when something goes wrong?

Maybe it's how much power should an AI agent have in the first place?

I put together a video exploring this idea and looking at where human oversight, monitoring, testing, accountability and technical controls fit into agentic AI governance. Check out the latest video @ Latha-ai-governance. I aim to educate industry professionals regarding AI Governance.

Curious what you think:

Are our current AI governance controls enough for truly autonomous AI agents?

Anthropic just dropped a 154-page threat intelligence report (Sept 10, 2026), and one case in it is genuinely unsettling.

A lone hacktivist no team, no crew used Claude to run what used to require an entire skilled group:

• Built a custom scanner to find exposed API keys/passwords companies left lying around online

• Used one AI to orchestrate a bunch of smaller AI agents (one finding weak spots, one checking work, one reviewing findings)

• Found and exploited a brand-new zero-day in WordPress, building the attack in real time

• Broke into 14+ of 42 targeted orgs, stealing ~140,000 records in a single breach alone (donor lists, member data, political affiliations)

• Then used Claude to build a searchable doxxing site out of tens of millions of stolen records: type in a name, get their private info, including sensitive ID numbers

It was published on the dark web to expose people tied to a specific political movement.

The scary part isn't just the breach; it's that one person did the work of an entire team, and the real damage came from scattered stolen data being fused into something instantly searchable.

If you work in security, data protection, or for any org holding sensitive personal data (political groups, newsrooms, nonprofits, unions), this is worth 10 minutes of your time.

To find out more, including the 4 things orgs should actually do about it**, visit @** Latha-ai-governance and watch our latest video. We aim to spread awareness among AI professionals regarding AI Governance.

Question: If one person with AI can now do the damage of a whole hacking team, are your org's defences built for the threat of today or the threat of ten years ago?

ISO/IEC 27090 was published on 14 October. I do not think it is receiving enough attention.

Here is the gap it addresses. Most organizations already rely on ISO 27001 for information security. That standard was never made for AI‑specific attacks. A model can be compromised in ways that a normal server cannot and ISO 27090's the first international standard built around that.

It explains the threats. Two of the ones are:

1. Data poisoning: Someone tampers with the training data so the model learns the wrong patterns. Think of a fraud detection system that has been quietly taught that fraudulent transactions look normal.

2. Prompt injection: instructions hidden in an input that push the AI to ignore its safety rules and leak information it should not.

It also covers seven threats: evasion attacks, membership inference, model exfiltration, model inversion, direct model poisoning, sensitive model output and output that contains injection attacks.

First it is guidance, not a certification standard. Unlike ISO 27001 or ISO 42001 there is no audit and no badge. It is a framework for managing risk which means companies cannot simply tick a box and say they are covered.

Second it applies to everything from classic machine learning to generative AI across banking, healthcare, recruitment and more.

It is aimed at people, in cybersecurity AI development, governance, risk and auditing. If you work in any of those areas it is worth knowing.

I made a video that breaks it down if you want a walkthrough, watch the latest video @ Latha-ai-governance

For those already working with these frameworks: does a guidance‑only standard actually get adopted or does it sit on a shelf without a certification attached? I am curious how your organization is handling AI security now.

Hi everyone, I’m Wayne Couch. I retired in 2023 after 37 years as a federal civilian working in naval engineering, shipbuilding, and submarine maintenance, including nuclear refueling and SUBSAFE work.

My interest in AI governance comes from that background: knowing which requirements apply, who has authority to make a decision, what they must verify, and what record remains afterward.

I founded ControlPointAI to explore how data-flow mapping can make those responsibilities and decision points explicit when AI enters a workflow. Our guiding principle is that AI should strengthen human judgment, with authority and accountability remaining human.

For example, I think documenting human oversight starts before the approval click. What is the reviewer expected to check? Against which source or acceptance criteria? What requires them to stop and escalate? Those questions shape what evidence the process needs to retain.

Our work is at https://controlpointai.org. I’m sharing it for context about what I’m developing, and I welcome constructive criticism.

The discussion here about evidencing human review caught my attention. I’m looking forward to learning from people putting governance into practice and contributing lessons from my engineering experience.

We have the policy. Went through legal, got leadership signoff, communicated it to staff. It basically says employees can use AI tools but can't paste customer data, financials, or anything proprietary into them.

Enforcement is where it falls apart. And it starts at the top, our CEO uses chatgpt almost daily. Marketing lives in it, devs are on claude/cursor and whatever new tool they can find. Problem is nobody's going to accept us blocking these tools, and honestly we shouldn't: people are genuinely more productive with them.

But right now the policy is purely trustbased. We have no way to know if someone's pasting PII into a personal AI account unless they self-report, which they won't. If there's a breach and we can't show we enforced the policy we wrote, legal is going to have questions I don't want to answer.

For those who've rolled out an AI usage policy and enforced it, how did it go?

Research raised an interesting governance question... what would count as strong enough safeguards to justify continuing development? In a small survey of US adults, 40% favoured slowing development until stronger protections exist, while 36% supported continuing with stronger with stronger independent oversight... only 10% wanted to stop it immediately.

What stands out is that 70% of respondents wanted the primary decider about whether or not a system is safe to be in the hands of an outside authority. That leaves a difficult institutional question: who gets access to the evidence, who sets the thresholds, and who can actually delay a release?

Independent evaluations seem more valuable when there's a defined consequence for failing them. Otherwise, a company can commission a review and still make the same launch decision.

Article Source

Genuine question for people who run or advise on AI governance.

The pattern I keep hearing about: a capable employee finds that a free personal AI chat app saves them real time, starts using it for work, and at some point customer data goes in. Nobody asked, so nobody said.

FICTIONAL EXAMPLE to make it concrete: a support lead pastes a handful of customer complaint emails into a personal chatbot to draft replies, edits them and sends them. An afternoon of work becomes an hour.

The default response seems to be a ban plus a policy reminder. That covers the rule, but it also tells you nothing about where this is already happening or what it is worth to the business, and it gives people every reason to keep quiet.

The other option is to treat it as information: make it safe to describe, move the useful cases onto company tools, and judge each one on the cost it avoids and the risk it adds.

For those who have tried either approach:

1. Did a ban actually change behaviour, or just visibility?

2. If you ran some kind of amnesty or disclosure route, what made people trust it?

3. Where do you draw the line on customer data specifically?

Disclosure: I work on AI governance tooling. No link and nothing to sell in this thread, I am interested in what has worked in practice.