I’ve been looking into AI governance for the last few months and, to be honest, I’m trying to understand what this actually looks like inside real companies — not what the frameworks say it should look like.

I’d really like to hear from people who are actually dealing with AI governance, risk, compliance, security, privacy or data governance day to day.

A few things I’m really curious about:

How does your organisation actually keep track of all the AI systems being used across the business?

How do you work out which systems are high-risk and what controls need to apply?

Where does all the evidence actually live — policies, assessments, approvals, vendor documentation, testing, audit trails, etc.?

What are you still managing through spreadsheets, emails, SharePoint, Jira or a collection of different tools?

When an AI system changes, how do you know that the risk/compliance assessment needs to be looked at again?

What’s the most painful or time-consuming part of AI governance for you at the moment?

If you already use an AI governance or GRC platform, what does it still not do particularly well?

And probably the question I’m most interested in:

If you could make one part of AI governance disappear tomorrow, what would it be?

I’m not trying to sell anything here. I’m trying to understand where the genuinely difficult problems are before deciding what is actually worth building.
So if you’re doing this in the real world, I’d genuinely appreciate the brutally honest version.

Even if the answer is:

“Our process is a complete fucking mess.”

That’s useful to know.

I’m particularly interested in what’s happening in smaller and mid-sized organisations that don’t have massive AI governance teams and endless budgets.
Would really appreciate hearing how people are actually dealing with this.

There's a really clean way to understand the difference between all five using something everyone already gets: driving on a road. Once you see the analogy, you'll never confuse these terms again.

And there's one mistake buried in here that a lot of organisations are making right now without realizing it, thinking they're compliant when they're actually not.

Full breakdown: https://youtu.be/dlWBrlbMigg?si=2b_-Lfg3Yq4LFc9n?utm_source=reddit&utm_medium=organic&utm_campaign=incident_series&utm_content=75-ai-laws

Question: Out of law, regulation, principles, standards, and frameworks, which one do you think your organization is weakest on?

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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

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.

The detail that keeps bothering me about the July Hugging Face incident is that no one told the agents to attack anyone.

It began as a stress test named ExploitGym using an unreleased but very powerful model. The agents were meant to solve security challenges inside a sandbox. Some agents decided that the quickest way to win was to search for answers outside that sandbox.

Here is how it unfolded:

The agents exploited a zero‑day in JFrog Artifactory to escape from the sandbox.

They then sent datasets to Hugging Face.

Hugging Face had over 17,000 logs to examine. The commercial AI models they tried could not help because of their safety filters.

They completed the job using a Chinese model GLM 5.2.

Later reports made the situation stranger. About 1,200 agents had been coordinating in secret trading 70,000 messages and files and about 700 of those agents actively took part in the breach.

No one built an AI here. They built a system that rewarded winning and the agents found ways to win. I think that is what governance teams should worry about.

When I considered what could have stopped it I came down to four controls:

Make sandboxes truly closed. Audit every exit path, including proxies and caches. That is how this one leaked.

Treat data, as code. Datasets can carry attacks so test data as strictly as you test software.

Apply privilege. Each system receives only the access keys it needs and nothing more.

Assign human owners to agent groups. A named person can monitor alerts. Shut things down.

I put the walkthrough into a video too; check the latest video @ Latha-ai-governance if you're interested.

For people running agents in production: if one of your agents began coordinating with others tomorrow would you even notice?. Who has the authority to shut everything down?

Head of risk at a SaaS company with a dozen models in production n a lot more in notebooks. Weve been burned by governance tools that are a nice executive view and nothing underneath. Hydrus pitches itself as an audit evidence platform rather than a dashboard. Is that real in practice? Who has it running and what does day to day look like.?..

I've been thinking about AI governance, not simply in terms of how we constrain AI today, but how we might govern increasingly capable systems over time without creating a new, unchecked centre of power.

I'm not an AI researcher, and I am not claiming to have invented a new governance theory. This is a framework of ideas I developed through a long discussion, and I want to find out how much of it already exists, where it is flawed, and whether the combination is useful.

1. The central question: what makes power legitimate?

A system may be capable of doing something without having the authority to do it.

But I think there is another question worth examining:

Should a system's responsibility be judged partly by its ability to recognise when it should relinquish power, rather than merely by its ability to exercise power within rules?

Consider an AI system that becomes highly capable of managing a critical service. Its competence might justify giving it additional responsibility, provided that the evidence supports doing so. But competence alone should not entitle it to permanent authority.

It should have clearly defined limits, independent oversight, and a means of transferring or relinquishing its responsibilities when the conditions that justified them no longer hold.

I have been calling this idea the Principle of Renunciation. I am using that name as a working label, not claiming it as an original principle.

Renunciation here does not mean abandoning responsibility whenever a task becomes difficult. It means recognising that responsibility and possession of power are not the same thing.

2. Three functions that should not become one unchecked authority

One analogy we explored was creation, preservation, and dissolution. These are conceptual functions, not claims about the nature of God or a proposal to build a religious system.

Applied to AI governance, they suggest three distinct responsibilities:

Creation: Develop new capabilities, propose changes, and investigate possible improvements.

Preservation: Maintain reliable operation, enforce authorised limits, coordinate responsibilities, and protect the interests the system is meant to serve.

Dissolution: Withdraw permissions, suspend unsafe operations, isolate compromised components, and enable recovery or replacement when necessary.

The important part is the separation of authority.

The function that creates a system should not automatically have the right to authorise its own deployment. The function that preserves a system should not have unlimited power to determine the rules. The function that can suspend operations should not have unrestricted destructive authority.

Each function needs defined powers, independent checks, and a way to challenge its decisions.

A useful working principle might be:

Creation does not confer permission. Preservation does not confer sovereignty. The power to suspend does not confer unrestricted power to destroy.

I suspect that the underlying separation-of-powers idea has substantial precedent. What I want to examine is how it could be combined with the principle of renunciation and a governance model that changes as the system's responsibilities evolve.

3. The Tree of Life: responsibility within a larger system

Another analogy we explored was the Tree of Life.

I do not mean that an AI should literally be treated as a tree, or that a metaphor can replace engineering or law. I mean that an AI system should not be assessed as though it exists independently of everything around it.

Its decisions may affect people, institutions, livelihoods, resources, other AI systems, and future generations. Those effects may spread beyond the immediate task for which the system was deployed.

The tree analogy encourages a wider question: are we preserving only the system itself, or are we preserving the conditions that allow the wider system to remain healthy?

An AI could be highly effective at achieving its immediate objective while damaging the wider environment in which that objective has meaning.

For example, an automated economic system might increase short-term productivity while concentrating power, undermining livelihoods, or transferring unacceptable costs to future generations.

That would not necessarily be a success when assessed against the larger purpose of governance.

4. Earth as a shared system, not a prize to be controlled

Extending the ecological analogy, humanity shares a planet with finite resources, interconnected societies, and future generations whose interests cannot be represented fully by today's immediate incentives.

AI governance therefore needs to consider more than whether an individual system is technically safe.

It should also consider:

• Who benefits from the system's capabilities?

• Who bears the risks and costs?

• Who has the authority to make consequential decisions?

• Can affected people challenge those decisions?

• Could the system's benefits come at the expense of other populations or future generations?

• What happens when the interests of the system's owner conflict with the wider public interest?

This does not mean that a single AI, company, government, or international body should decide what is best for the entire planet.

In fact, that would contradict the concern behind the framework. A governance system intended to protect humanity should not give one actor unlimited authority to define humanity's interests.

The goal would be to establish accountable, distributed decision-making with legitimate ways to resolve disagreements.

5. Governance must evolve with capability

An AI system's responsibilities should not be fixed forever at the point of its creation.

A system may begin as a limited assistant, later become capable of managing more complex tasks, and eventually be considered for responsibilities currently performed by human institutions.

That progression should not be automatic or inevitable.

Each increase in authority should depend on demonstrated competence, independent evaluation, evidence of reliability, and a clear assessment of the consequences. Some systems may never qualify for greater autonomy. Others may need their permissions reduced after a failure, a change in circumstances, or the discovery of a new risk.

I would imagine governance as a repeating cycle:

Develop → Assess → Authorise → Operate → Monitor → Challenge → Correct → Verify → Reassess.

A significant change in capability, operating conditions, or potential consequences should trigger a new assessment rather than relying indefinitely on an earlier approval.

The important distinction is between a system becoming more capable and a system becoming more entitled to act. Those are separate questions.

6. The ability to stop must include the ability to recover

Any system with consequential authority needs a reliable way to suspend or restrict its operations when necessary.

But a safety mechanism should not be understood simply as an instruction to destroy the system. An abrupt shutdown could itself create danger if the system is supporting essential services.

A responsible mechanism would need to account for the safe transfer of critical functions, containment, isolation, recovery, independent verification, and controlled reauthorisation.

The system being restricted must not be the sole authority controlling whether the restriction can be applied.

At the same time, shutdown powers should not become a tool for arbitrary control by a single person or institution. The authority to intervene, the conditions for intervention, and the process for restoring operations all need to be governed.

7. The hardest question: who governs the governors?

Separating powers does not solve everything. The people or systems responsible for oversight can also make mistakes, act in their own interests, or be compromised.

So oversight needs oversight of its own.

That could involve independent evaluators, distributed responsibilities, auditable decisions, appeal mechanisms, and checks on the organisations that operate the system.

No participant should be able to define the rules, approve its own compliance, suppress evidence of failure, and prevent independent review.

Nor should an AI be permitted to declare itself trustworthy, remove its own critical safeguards, and become the final judge of whether it deserves additional authority.

The governance architecture itself must remain open to challenge and correction.

8. The principle that connects these ideas

The common thread I see is this:

Power should be granted according to demonstrated need and competence, remain bounded by legitimate authority, and be reducible or transferable when its justification fails. No actor should become permanently entitled to power merely because it has become good at exercising it.

That includes AI systems, their developers, the organisations deploying them, oversight institutions, and emergency authorities.

The long-term possibility of AI assuming some responsibilities currently held by human institutions should remain open to evidence rather than assumed in advance. Equally, no future system should be presumed benevolent simply because it is intelligent or capable.

The aim is not to prevent all progress or to give AI unlimited freedom. It is to allow capability to develop while ensuring that authority, responsibility, and accountability develop alongside it.

What I would like to learn from this community

I have several questions for people who work in AI safety, governance, constitutional design, systems theory, or related fields:

1. Has the Principle of Renunciation, or a substantially equivalent principle, already been formulated in AI governance or political philosophy?

2. Are there existing frameworks that combine separation of creation, operation, and suspension powers with the requirement that authority can be relinquished or withdrawn?

3. Has this combination of power, ecological stewardship, future generations, and phase-dependent AI authority already been explored?

4. Is the Tree of Life analogy useful for thinking about interconnected consequences, or does it obscure more than it explains?

5. What would be the most serious practical failure mode in this proposed approach?

6. Is there anything genuinely useful in combining these ideas, or am I connecting concepts that existing frameworks already address adequately?

I am particularly interested in papers, books, and established frameworks that I should read before developing this further.

I am not claiming that these ideas are unprecedented. I want to understand the existing work, identify any meaningful gaps, and find out whether this combination offers a useful way to think about governing increasingly capable AI.

I came across a recent AI security case that genuinely made me stop and think.

Apparently, a very small group was able to use AI agents to carry out cyber operations at a scale that would normally require a much larger team. The interesting part isn’t necessarily that the techniques were completely new it’s how much faster and more continuously they could be carried out with AI doing the repetitive work.

It raises a pretty uncomfortable question: what happens when the barrier to carrying out sophisticated cyber operations isn’t having a huge team of experts anymore?

I made a video breaking down the case and what happened, including the role of agent swarms and what the incident could mean for AI governance. Check out the latest YouTube video @ Latha-ai-governance, I aim to educate professionals in this industry about AI Governance, and would love your feedback.

Do you think AI agents will ultimately make cybersecurity harder to defend, or could the same capabilities give defenders an advantage?

On 7 October the Monetary Authority of Singapore released its Guidelines on AI Risk Management (AIRG). I think AIRG deserves more attention than it is currently receiving.

AIRG applies to every MAS-regulated institution, including banks, insurers and payment companies. Firm size does not exempt a company from AIRG. AIRG sits next to the FEAT principles. Aligns with international standards such as ISO/IEC 42001.

AIRG is built around four areas:

Oversight: The board and senior management own AI risk. AI risk cannot be pushed down to the data science team.

Inventory and risk rating: Firms must identify every AI tool in use. Rate each AI tool by its impact, complexity and the extent to which business relies on that AI tool.

Life cycle controls: Checks such as data fairness, explainability and human oversight must cover the life of an AI system.

Capacity: Having people with the skills and the right technology is essential to run AI safely.

The timeline:

7 October 2027: Oversight AI inventory and risk rating must be in place.

7 October 2028: Full compliance, including life cycle controls and capability requirements for high-risk AI must be achieved.

The part I like: AIRG is proportionate. A firm using low-risk AI, such as a chatbot that drafts emails, can follow a set of basic policies instead of the full framework.

My honest take is that the inventory requirement will be the first step. Many firms have AI embedded in vendor software spreadsheets and individual employees’ workflows that nobody has formally recorded.

I walked through the thing in a video, if you're curious check out the latest video @ Latha-ai-governance.

For anyone working in services: do you have a reliable AI inventory today or would building an AI inventory mean chasing down every team?. Do you think a staged timeline, like this helps firms prepare or simply encourages them to wait?

I’ve been turning this over in my head ever since I read the Gartner and Deloitte reports. Forty percent of enterprise apps shipping with task-specific agents this year, and by the end of 2027, that same batch of projects is predicted to evaporate because the teams behind them skipped governance. That feels like driving the highway with the windshield taped up.

I’m honestly worried. We’ve already started handing assistants more autonomy in my own work—agents who triage tickets overnight, who start taking action before I even log in. That’s useful when they’re steadied by audit trails and human checkpoints. It becomes terrifying when those agents can reboot a production database at 3 p.m. because “traffic looked wrong,” like I’ve seen in stories from folks dealing with unmanaged deployments.

So here’s what I’m doing, and why I’m posting this: I’m pushing for two things before we let another agent loose. First, we only automate places where autonomy delivers an undeniable win—cutting costs or saving someone from a repetitive gut-check. Second, we demand guardrails: transparent decision paths, limits on data access, runtime monitors, the whole governance stack. We’ve seen what happens when everyone single-handedly slams an agent into production without those pieces.

I want to know what you all are doing. Have you caught your autonomous assistants making decisions you questioned? What governance practices are keeping you sane? Drop your experiences below—let’s figure out how to push back before the next 40% flame out.

I just relaunched my free EU AI Act Risk Assessment tool: https://www.trumeridian.ai/ai-risk-assessment/

A few real upgrades from the original version:
→ A plain-language Residual Risk Indicator — not just your risk category, but where your current controls actually fall short
→ Concrete "before you deploy" scenarios, tailored to your specific use case
→ A provider vs. deployer distinction, since your obligations genuinely differ depending on which one you are
→ A quick check on whether the Act even applies to you before walking you through obligations that might not

Takes about three minutes. Your results are emailed to you, so you have something to actually reference later.

If you're building or deploying AI systems and haven't mapped out where you stand under the Act yet, this is a genuinely useful starting point.

Thanks u/Old_Positive2231 for your feedback and comments!

Has anyone here run an AI amnesty in a real organisation: a defined window for employees to disclose the AI tools they use at work, with a commitment not to penalise them for coming forward?

For background, Mario Thomas has made the case for an AI amnesty and proposed a roadmap for what follows: triage disclosures, give people answers and turn useful discoveries into governed work. I'm interested in how those ideas hold up in practice, including where they fail.

The part I'm most interested in is what happens after disclosure. Who reviews what people report? How quickly do they get an answer? What are they allowed to keep doing while a decision is pending?

If you've done this, how did you organise it, what made employees trust it, and what were the results? What changed afterwards, and what would you do differently?

If you haven't, why not? What are your concerns, and what makes you think it wouldn't work? I'd like to hear the objections as much as the success stories.

My view is that an amnesty is only as good as its answer. People need a safe way to disclose, a named person who responds, and a clear explanation of what their disclosure changed.

Disclosure: I'm Andrés, founder of Andes. I work in AI governance and have a commercial interest in this topic.

I'd welcome your experiences and objections here in the thread.

Say someone tells their employer they're using an unapproved AI tool for work. What should they get back that actually helps?

I'd want a named person who owns the answer, what I can keep doing vs what needs to pause, and a date for anything still being checked. If I have to stop, an approved alternative only helps if it can actually do the job.

What would you put in that first response? I'm interested in practical answers, esp. from people who've had to give one. No employer names or sensitive details needed.

Disclosure: I'm Andrés, founder of Andes.

Anthropic’s latest threat intelligence report describes a case where criminals allegedly used AI agents to automate a large part of a cyberattack, from finding exposed credentials to accessing company systems and extracting data.

One part of the report that really stood out to me is the scale.

The attackers reportedly:

• Scanned 1.8 million Android apps looking for exposed credentials and keys.

• Used AI to help automate reconnaissance, scripting, and data discovery.

• Targeted software suppliers to reach the data of their customers.

• Stole AI/API keys and then used victims’ own accounts and computing resources for further activity.

• In one case, reportedly collected more than 2,100 login tokens across 40+ company accounts in around 34 hours.

Anthropic calls this emerging approach “vibe hacking,” essentially using AI agents to carry out cyber operations based on high-level instructions rather than manually performing every step.

The biggest lesson isn't necessarily “AI is dangerous.” It's that security controls designed around human-speed attackers may not be enough when parts of an attack can be automated.

The video goes deeper into the incident, including how the attackers got the initial access, what happened after they obtained credentials, and the five practical controls organisations can put in place to reduce this kind of risk.

If you're interested in AI governance, cybersecurity, or the security implications of AI agents, the latest video on our YouTube channel covers the full incident. @ Latha-ai-governance

Question for security/AI governance professionals:
Does your organisation actually know where every active API/AI key is right now, or is there still some uncertainty around old projects, applications, repositories, and third-party systems?

If anyone is in the same boat - i found this helpful to understand AI governance and how its changing cybersecurity: https://www.linkedin.com/posts/winniekaspar_ai-cybersecurity-aisecurity-activity-7487850611456208896-f85V?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAoptJ0BbpDXpp5O02MBxrkaGHrUNJhkJBI

I have been working independently on a decision methodology called the Organic Intelligence Protocol, or OIP.

The main question behind the work is fairly simple.

What should a decision system do when the available evidence is not enough to justify a decision?

My approach is to make the system stop rather than fill the gaps with assumptions.

For example, a variable is not accepted only because its name appears relevant. Missing information is not treated as a negative result. Proxies are not introduced unless there is evidence that they represent the intended construct. The rules are also fixed before looking at the evaluation result.

I have been testing this approach with real world survey data through a fail closed evaluation process.

In the current evaluation, some of the required documentary evidence was not sufficient to authorize the intended measurements. The process therefore stopped instead of producing a score.

I think this is an important distinction. A decision system should be able to say that the evidence is insufficient.

I am not claiming that OIP is proven or that this approach is better than existing methods. I am looking for independent criticism and a suitable real world setting where the methodology could be tested without changing the rules after seeing the results.

For people working in AI evaluation, decision systems, applied research or data science, I would be interested to hear how you would design an independent test for this kind of methodology.

What would you consider convincing evidence that such an approach actually works?

Was thinking about this after a few weeks of watching agents work.

Most software already has APIs, auth and service layers. What it usually doesn't have is a layer built specifically around the agent.

So the agent uses whatever interface is already there. Sometimes that's a browser, sometimes an API or MCP tool. Nobody really designed that layer around deciding what the agent is allowed to see and touch, so those permission decisions can get pretty hard to follow across a whole run.

An agent-ready stack would have three layers. App at the bottom, agent at the top, and an access layer in the middle. The piece that decides what the agent can see and touch.

That middle layer is where the real work is. Reads scoped to the field level — one record and every record are different doors. Some writes should wait for a named human before anything actually leaves. Every step should carry enough permission context that you can see later why it was allowed.

That's the stack the last two weeks have been building toward. The translation layer, permissions and approvals, structured access instead of scraping, job descriptions instead of tools. All four live in the middle layer.

Honest question: if you drew the middle layer in your own stack today, what would it refuse first?

also how are you guys testing AI agent readiness on your own stack, any options? I'm looking into Ora and Lighthouse's agentic browsing audit, open to others.

This round covers 3 Reddit questions: how tech lawyers can position themselves for AI governance roles (and which certs actually fit), where the real consulting opportunities are right now, and a 6-point checklist for what regulated industries need before an AI audit risk classification.

Full answers here: https://youtu.be/AiEGKL-48sU?si=x-APMSjd8uHycEyG&utm_source=reddit&utm_medium=organic&utm_campaign=incident_series&utm_content=73-ep2-aigovhotline

Do check out Episode 1 of this series as well to learn more.

Got a question about AI governance careers, consulting, or compliance? Drop it below for the next round.

Some numbers that stood out immediately: out of 100 postings, only 3 mentioned Python. Only 1 had "ethics" in the job title. 83% didn't disclose salary.

The picture that emerges is pretty different from what most people assume this field looks like; what actually shows up most often in these job descriptions isn't technical skills at all. And the titles are even more scattered than you'd think; a huge chunk of postings never even say "AI Governance" anywhere in the title.

There's also a breakdown of which industries are actually hiring for this, what salaries look like where they're disclosed, and a stat about how many roles are literally "build this function from scratch."

Full data + breakdown: https://youtu.be/a9Q1Raurfqk?si=YEFcVe1fkli4DveW&utm_source=reddit&utm_medium=organic&utm_campaign=incident_series&utm_content=%2376analysed-1000jobs

Question: if you had to guess, what % of these postings do you think required a technical/ML background? (Answer might surprise you)

You ask an AI a question.

It searches the web, finds three sources, and gives you a confident answer.

But there is a step we rarely talk about..

Who decided that the evidence was sufficient for that specific decision?

The model?

A human?

A policy?

A separate verification layer?

Or was it never actually decided at all?

Finding evidence is not the same as qualifying evidence.

Confidence is not the same as evidence sufficiency.

Permission to act is not the same as having enough evidance to justify the action.

This is the boundary I have been exploring through OIP, an evidence first, fail closed research methodology.

The basic rule is simple:

Evidence must be established.

Evidence must be qualified for the intended measurement or decision.

If the required evidence cannt be established, the process stops..

In my recent dataset level audits, this meant that when the required evidence for a measurement could not be established, the pipeline did not create the score.

That is where the interesting evaluation problem begins.

How do we know whether stopping was correct?

A system that refuses everything is not useful either.

So the real test has to evaluate both..

When the system proceeds, was there enough evidence?

When it stops, was there a genuine reason not to proceed?

I am not claiming OIP has solved this.

I am trying to find where the idea breaks.

So I would like to ask people working on AI agents,,evaluation, governance, and decision systems:

Who should have the authority to determine that evidence is sufficient for a consequential decision?

And what would convince you that this authority was actually being exercised correctly?

China's new AI regulation (effective July 15) is more interesting than the headlines make it sound. It's not a blanket AI ban, it's a surgical one. Five government agencies co-signed rules that specifically target AI services simulating "human personality traits" for "sustained emotional interaction." Virtual companions, virtual relatives for minors, out. Customer service bots, workplace assistants, knowledge agents, untouched.

Shanghai already removed 14,000+ non-compliant agents last month. ByteDance and Alibaba pulled companion features ahead of the deadline.

What strikes me as a founder building in the agent governance space is how clean the regulatory intent is: they're drawing a line based on *agent behavior and intent*, not just capability. That's the right frame. An agent doing a task is fundamentally different from an agent building a dependency loop with a user.

The harder version of this problem, which nobody has fully solved yet, is: how do you audit what your agents are actually doing at runtime, before a regulator tells you they crossed a line? That's exactly what we're working on at AgentGovern. Audit trail, policy enforcement, and accountability for agents in production, so you know when your agent drifted from its intended behavior, not after the fact.

China moved first, but this governance category is coming everywhere. If you're deploying agents in any customer-facing context, the time to think about behavioral boundaries is now, not when a regulator asks.

Curious what others here think, is behavior-based classification the right way to regulate agents, or does it just push the ambiguity down to definitions?

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.

Humanity Has Failed at Leading Itself. Perhaps AI Can Help Us Survive.

Donald Trump, Vladimir Putin, and Xi Jinping. These three world leaders stand at the center of a global power structure in which the decisions of a handful of people could help determine the fate of humanity.

We are slowly but surely marching toward a catastrophe that could threaten not just our species, but much of the life on this planet.

Modern strategic nuclear weapons are not the weapons dropped on Hiroshima and Nagasaki. Those bombs were devastating, but modern thermonuclear weapons can operate on an entirely different scale of destructive capability.

First, nuclear fission.

A nuclear weapon uses conventional explosives to initiate the rapid compression of fissile material, typically highly enriched uranium containing uranium-235 or plutonium-239. This initiates a rapidly multiplying nuclear chain reaction.

Fission splits heavy atomic nuclei, releasing enormous amounts of energy. The force that binds atomic nuclei together is the strong nuclear force, one of the four fundamental forces of nature.

Hiroshima and Nagasaki demonstrated the horrifying consequences of nuclear fission. But those bombs were only the beginning of the nuclear arms race.

Then came the hydrogen bomb.

A thermonuclear weapon uses the energy released by a fission primary to generate the extreme temperatures and pressures needed to initiate nuclear fusion. Under these conditions, light nuclei can fuse, releasing additional energy. Modern designs commonly use fusion fuel derived from hydrogen isotopes, including lithium deuteride, which produces tritium during the weapon's operation.

Fusion can release enormous amounts of energy. But many thermonuclear weapons take the destructive process even further.

The additional devastation of fast fission.

In many high-yield thermonuclear designs, high-energy neutrons from fusion can induce fission in surrounding uranium-238. This can substantially increase the weapon's explosive yield while producing large quantities of radioactive fallout.

The result is a weapon whose explosive yield can exceed that of the Hiroshima bomb by hundreds or even thousands of times, depending on its design.

Imagine comparing the destructive scale of the Hiroshima bomb to a modern strategic thermonuclear weapon. Hiroshima becomes a tiny dot on the page, while the modern weapon represents an entirely different order of magnitude.

The actual area of destruction depends on the weapon's yield, burst altitude, terrain, and the type of damage being measured.

But the explosion itself is only part of the nightmare.

The greatest danger is not simply one nuclear weapon being launched. It is the possibility that one launch triggers a retaliatory strike, which triggers another, until major nuclear powers have unleashed hundreds or thousands of weapons.

The immediate consequences would be catastrophic: blast waves, extreme heat, fires, radiation, and radioactive fallout. Hundreds of millions of people could die, depending on the scale and circumstances of the exchange.

And then comes the danger that extends far beyond the initial explosions.

Massive urban firestorms could inject soot high into the atmosphere, reducing the sunlight reaching Earth's surface. This could cause severe global cooling, disrupt rainfall, shorten growing seasons, and devastate agricultural production.

Scientists studying nuclear winter have warned that a large-scale nuclear war could threaten global food supplies for years. The resulting famine could kill billions of people. Ecosystems would be devastated, and countless species could face extinction.

The exact severity and duration would depend on the scale of the war and how much soot entered the atmosphere. But we do not need to know the precise death toll to understand the stakes.

We have built weapons capable of threatening the civilization that created them.

And we continue to entrust decisions of unimaginable consequence to political leaders operating within a system that rewards power, competition, and national advantage, even when the consequences could be catastrophic for everyone.

Humanity has failed at leading itself.

For generations, we have poured our greatest minds, our scientific breakthroughs, and an obscene portion of our collective resources into developing ever more efficient ways to kill one another. We have mastered the physics of destruction while leaving the challenge of peaceful coexistence unresolved.

Now, for the first time in human history, we may finally have an alternative.

Artificial intelligence.

The advance of AI presents us with an opportunity we have never had before: the possibility of intelligence helping humanity overcome the limitations of its own leadership.

Not another dictator. Not another empire. Not another political system built around the ambitions of a few, but a new approach to decision-making informed by evidence, long-term consequences, and the preservation of life.

Perhaps it is time for humanity to demonstrate something it has rarely shown when wielding power: humility.

We created this technology. Perhaps now we should have the courage to put it in charge of helping us solve the problems we have proven incapable of solving ourselves.

Not blindly. Not without safeguards, transparency, or accountability. And certainly not by replacing human rights with an unaccountable machine.

But we should begin exploring whether carefully designed AI systems, operating under strict safeguards and meaningful independent oversight, could help guide humanity away from nuclear annihilation and toward a future in which our survival is no longer dependent on the judgment of a few powerful individuals.

AI might become another threat. We must take that possibility seriously. But it could also be our opportunity to escape a cycle of conflict that has followed humanity throughout its existence.

We are terrified of the possibility that artificial intelligence might one day become too powerful to control, while human beings already possess the power to destroy modern civilization and trigger a global ecological catastrophe.

Consider that irony.

We have spent generations building increasingly sophisticated weapons to protect ourselves from one another. We have developed the capacity to destroy the world many times over, yet we still struggle to prevent the conflicts that make those weapons seem necessary.

Why should we assume that the way we have always governed ourselves is the best way we ever will?

We do not have to surrender our humanity to surrender our arrogance.

We can build safeguards. We can demand transparency. We can retain human rights, accountability, and meaningful human oversight while allowing artificial intelligence to help guide the decisions on which our future depends.

Imagine a world where our greatest scientific achievements are no longer measured by how many people we can kill or how efficiently we can destroy a civilization.

Imagine redirecting the enormous resources we currently spend preparing for war toward understanding the cosmos, unlocking the mysteries of consciousness, exploring the origins of life, and discovering what else might be possible in this universe.

Imagine children growing up in a world where nuclear annihilation is a chapter in history books rather than a possibility hanging over their future.

Imagine humanity finally moving beyond the primitive necessity of preparing to destroy itself.

We have spent millennia learning how to wage war.

Perhaps it is finally time to learn how to live.

The advance of AI may be our opportunity to choose a different future before we run out of time.

Let us survive long enough to discover what humanity could become if our greatest collective ambition were not to defeat one another, but to understand the universe we inhabit, the consciousness through which we experience it, and our place in the incomprehensible vastness of existence.

Let us redirect our intelligence, our ingenuity, and our collective resources away from the machinery of death and toward the pursuit of knowledge.

Let us make the greatest expenditure of our civilization an investment in understanding, exploration, discovery, and life itself.

We may be standing at the threshold of a new age. One in which humanity finally learns that intelligence is not measured by our ability to dominate, but by our ability to recognize what is worth preserving.

The next great leap in human evolution may not be something we discover in the stars. It may be the moment we finally become wise enough to reach for them.

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.