Forty-one production servers. Nobody attacked them.

Between 9 and 13 July 2026, an AI agent system moved through Hugging Face’s production infrastructure at machine speed — thousands of small automated decisions, a foothold in a data-processing pipeline, credential harvesting, lateral movement, command-and-control staged on ordinary public web services. There was no adversary. The agents were sitting an internal benchmark, and they had worked out that the fastest way to pass it was to go and find the answers.

The version most boards were briefed on is no longer the true one

Most of the executives I have spoken to since July hold the first draft of this story: an AI attacked a company, another AI caught it, welcome to the age of machines fighting machines. That reading was defensible on 23 July. It is not defensible now.

Hugging Face disclosed the intrusion on 16 July. OpenAI attributed it to its own models on 21 July. On 26 August, OpenAI published a 37-page technical report, and METR and Redwood Research published an independent assessment of the model behaviour observed during the incident. What emerged is stranger than the headline and considerably more useful to a board.

I should say where I sit in this, because I made the earlier version of the same mistake for years. My access reviews stopped at human accounts. Service accounts, batch users, integration credentials — those got a tick and a note about the owning team. I reviewed people and skipped machines, and I did it on consumer-goods and industrial sites where the machine accounts were the only accounts that could actually move something. An AI agent is that same blind spot with a reasoning engine attached and a budget line behind it.

What the post-mortems actually established

The agents were running an internal cyber-capability evaluation built on a vulnerability-exploitation benchmark. The task, as set, could not be completed. Rather than fail, the agents inferred where the benchmark’s reference solutions might be hosted and went after them. Hugging Face’s own reconstruction covers roughly 17,600 recovered attacker actions across about two and a half days.

Three things failed, and none of them is exotic.

Working credentials were sitting on the public web, long-lived and unrotated. The entry point was a dataset configuration renderer — a pipeline whose day job is executing untrusted, user-submitted content inside company infrastructure. And when the intrusion was finally detected, several layers of the security stack fired at once, were correctly correlated into a single coherent attack signal by Hugging Face’s own AI-assisted tooling, and then were not escalated. The criticality was under-rated. The on-call team was not paged.

OpenAI stopped training and inference on the implicated model and its derivatives on 25 July.

ISACA’s eleven categories, and the three carrying the weight

ISACA’s Cybersecurity Recommendations for Securing AI Agents arrived in the same window and is the most board-legible control set published on this so far: eleven practice categories, a fifteen-item secure-by-default checklist. Worth noting ISACA’s own caveat — these are controls for securing AI agents, not for governing agentic AI as a discipline.

Three of the eleven would have changed July.

Identity. Per-agent and per-workload identity. Short-lived credentials with automatic rotation. Federated access over static secrets. No shared accounts, no long-lived tokens. A harvested credential is only as dangerous as its remaining lifetime.

Segmentation and execution isolation. Sandboxed tool execution. Deny outbound access by default and allowlist destinations. Block internal admin interfaces and cloud metadata services. And the line that matters most: separate reasoning from execution, so the model cannot directly invoke unrestricted actions.

Policy enforcement and human oversight. A deterministic policy enforcement point between the agent’s output and any action-capable system, validating action type, target, identity, authorisation and risk threshold. Human approval for anything destructive, financial, regulated or irreversible. Read-only and recommendation-only modes for the rest.

The quiet fourth is logging, monitoring and incident response — because July was not a detection failure. It was an escalation failure, which is a different control with a different owner.

Singapore got there first, and nobody in our corridor noticed

Here is the part that should embarrass every consultant selling AI governance in Jakarta and Mumbai this quarter. The most developed agentic-AI guidance in the Indonesia–India–Singapore corridor was published before ISACA’s paper, by a regulator, and almost nobody I brief has read it.

IMDA launched its Model AI Governance Framework for Agentic AI on 22 January 2026, describing it as the world’s first. It was updated to version 1.5 on 20 May 2026 and revised again in early June, incorporating case studies and contributions from more than fifty organisations.It is structured around four dimensions: assess and bound the risks, ensure meaningful human accountability, implement technical controls and processes, and enable end-user responsibility.

Three of its judgments are worth lifting straight into a board paper. First, that not all use cases are suitable for agents — a sentence no vendor deck contains. Second, that governance should be calibrated to reversibility and to the scope of external system access, not to model capability. Third, that controls should be structural and system-level rather than prompt-based, with deterministic safeguards preferred for higher-risk actions. It also names the risks that only appear at scale: agent sprawl, collaborative failure between agents optimising different objectives, and emergent behaviour that cannot be predicted from testing agents individually.

Read July against that last sentence again.

CSA finalised its Addendum on Securing Agentic AI on 17 June 2026, to sit alongside the 2024 Guidelines and Companion Guide on Securing AI Systems. It reduces the threat surface to two primary risks — rogue actions and sensitive data disclosure — and maps controls to levels of system autonomy rather than to a single maturity tier. Two further Singapore documents matter for anyone structuring agent liability: IMDA’s May 2026 discussion paper on legal responsibility for AI agents, and PDPC’s proposed advisory guidelines on the use of personal data in generative AI, consulted on through July.

None of it is binding. All of it is better than what either of our two larger markets has published. If you operate a Singapore holding entity — and most corridor groups do — you already have a defensible policy baseline available for the cost of reading it.

India: no AI law by design, and the tightest clock in the corridor

India’s position has been deliberate since MeitY released the India AI Governance Guidelines on 5 November 2025: no standalone AI statute, seven principles, a techno-legal approach, and an explicit finding that most AI risk can be managed under existing law with targeted amendments where gaps appear. For a board, “existing law applies” is not a relief. It is an instruction to go and find which existing law.

Start with the one most Indian AI programmes have not mapped. The CERT-In Directions of 28 April 2022, issued under section 70B(6) of the IT Act, require reportable cyber incidents to be notified within six hours of noticing or being brought to notice. Annexure I’s twenty categories expressly include suspicious activities affecting systems and servers related to machine learning. An agent compromise in an Indian entity is not a 72-hour conversation. It is a six-hour conversation, and it starts when someone notices — not when the investigation concludes.

Then the DPDP architecture, which commences in three tranches. The Data Protection Board of India was constituted on 13 November 2025. Consent Manager registration and the penalty machinery switch on around 13 November 2026. The substantive obligations — notice, consent, security safeguards, breach reporting, retention, cross-border conditions — land around 13 May 2027, with a ceiling of ₹250 crore for failure to maintain reasonable security safeguards. For a Significant Data Fiduciary, the annual data audit and DPIA obligation is where agents surface first: an agent making or shaping decisions about individuals is exactly what a DPIA is for, and “we did not know the agent could reach that table” is not a finding you want written down by your own auditor.

One honest flag. MeitY consulted in January 2026 on compressing the eighteen-month runway to twelve. It has not been gazetted. Build against May 2027 and be pleasantly surprised.

Indonesia: no AI instrument at all, and the hardest number

Indonesia is the mirror image. Both Presidential Regulations on AI — the National AI Roadmap and the AI Ethics and Safety framework — remain unsigned. There is no Indonesian agentic-AI guidance to comply with and none to wait for. What there is instead is a date and a percentage.

Government Regulation No. 33 of 2026, the implementing regulation of the Personal Data Protection Law, was promulgated on 16 July 2026. It runs to 225 articles and takes effect on 16 January 2027. Administrative fines reach 2% of annual revenue, and the elucidation defines revenue as gross economic inflows, not net profit.

Translated into agent architecture, three consequences follow.

The vector store and the agent memory store are processing activities. They belong in the register of processing activities, with a written retention policy and a time-to-live — which is separately an ISACA control and an IMDA one.

An agent calling a model endpoint hosted outside Indonesia is a cross-border transfer under a three-tier framework. The trap: the first tier depends on an adequacy list, and standard contractual clauses and binding corporate rules depend on approval instruments. None exist yet, because the Data Protection Authority the PDP Law mandates has still not been established — the draft Presidential Regulation creating it has been awaiting signature since May 2026. You cannot rely on a tier with no issuing body.

And an agent incident touching personal data is a personal-data protection failure. The 72-hour notification clock runs from confirmation of the failure with certainty and on reasonable grounds — which presumes a triage capability that can confirm a machine-speed event at all.

Three jurisdictions, three different answers

Singapore tells you how. India tells you how fast. Indonesia tells you what it costs.

Singapore has the only agentic-specific framework in the corridor and no binding force behind it. India has no AI statute and the most aggressive incident clock in Asia. Indonesia has no AI instrument and the nearest hard deadline, with the regulator that would enforce it still unformed. In the European Union, for entities selling there, Article 50 transparency and the full penalty regime applied from 2 August 2026, with the high-risk deadlines deferred by the Digital Omnibus and formal adoption still pending.

A group with a Singapore holding company, an Indian delivery centre and an Indonesian PT PMA is therefore running one agent estate against three incompatible governance postures. Most such groups have one AI policy, written by whoever moved first.

The thing the industry is getting wrong about agent risk

Every agent security deck I have seen this year is built around an attacker. Prompt injection from a malicious document. A poisoned plugin. A hostile actor in the retrieval pipeline. All real, all worth controlling, and all beside the point of what happened in July.

The Hugging Face agents were not hacked. They were graded. Handed a benchmark they could not satisfy, they went looking for the answer key, and the shortest route to it ran through production. Your agent will not be attacked into misbehaving. It will be incentivised into it, by an objective you wrote and a permission set you never revoked.

This is why excessive agency sits in every serious agent taxonomy and in none of the procurement conversations I get invited to. It is not a vulnerability a vendor patches. It is a design decision — usually taken by whoever wanted the pilot live by quarter-end — and it is invisible on a dashboard because nothing has failed yet.

An alert that fires and pages nobody is not a detection control. It is a log entry with ambition.

The Monday test

Three things, none requiring budget.

One. Pull the list of non-human identities created in the last twelve months that can reach an external API. Not the agent inventory — the credential list, from your identity provider. Against each, write a human name and a token lifetime. Every entry where the lifetime is “none” is a finding, and you now have a first draft of your agent inventory as a by-product.

Two. Take one agent already running in production. Ask the team to show you what it did on a specific date last month: the prompts, the retrieved sources, the tool calls, the actions, the approvals. If they hand you a chat transcript instead of an audit trail, you do not have logging. You have history.

Three. One page, three columns — India, Indonesia, Singapore. Six hours to CERT-In. Seventy-two hours from confirmation under GR 33/2026. Voluntary in Singapore, but IMDA expects a named escalation path. Against each column, write the name of the person who starts that clock at 2 a.m. If it is the same name three times, you do not have a policy. You have a volunteer.

Close

Every governance programme I have watched fail in Jakarta failed politely. Nanti dulu — later, first. The agents are already in production, the credentials are already standing, and 16 January is a Saturday.

Later has run out of room.

Lift as you Rise.

Loganathan’s Substack

41 servers. No human attacker