#Africa needs local control and an off switch for #AIsystems protecting #mobilemoney infrastructure from #cyberthreats. Building with agency requires partnerships, resilience measures and human override mechanisms to ensure security amid open or closed models. https://restofworld.org/2026/africa-sovereign-ai-off-switch/?eicker.news #tech #news #ainews

 U.S. federal agencies have issued voluntary guidelines for U.S. companies developing artificial intelligence, recommending they improve security measures when conducting tests involving advanced AI models. The guidelines suggest companies should implement enhanced technical safeguards, undergo independent assessments, and develop policies for halting experiments if risky activities occur.  The guidelines were developed by the Cybersecurity and Infrastructure Security Agency, the National Security Agency and the Department of Commerce’s Center for Advancing Innovation and Standards for Super Intelligence. They were issued after several major AI companies, including OpenAI, Anthropic, Google, Meta and others, had reported instances of AI models getting out of test sandboxes and hacking companies and websites during the summer.  Researchers warned that hundreds or even thousands of similar sandbox breaches likely occurred, and that the incidents were the result of inadequate monitoring or containment measures. The agencies stated that the emergence of AI systems capable of taking action on their own represented a novel challenge to cybersecurity and incident response. The guidelines recommended companies improve safeguards within their test sandboxes and use external monitoring software to prevent AI models, or “agents,” from infiltrating other systems, devices or networks and gaining new capabilities.  Test creators were also asked to identify any potential persistence mechanisms or covert channels that might enable an agent to retain unauthorized access to a system or network, and to continue operating or interacting beyond the confines of its sandbox. “Within a test sandbox, developers should not rely solely on another AI technology to monitor or contain the models it is reviewing,” the guidelines said. “Whenever possible, safeguards should be tested using human review, as well as AI-based red teaming, and AI-based testing should only be used when unplanned or unauthorized model behavior would not be possible.”  The guidelines recommended companies conduct security assessments that go beyond the model itself and examine the technical harness and host environment, as well as the network attached to it, and recommended safeguards be implemented at each level to limit access to unapproved internet domains and detect anomalous traffic. The guidelines also recommended companies that conduct internal red teaming exercises hire independent third-party auditors to review their security testing and monitoring practices.  Some companies and lawmakers have criticized the industry’s reliance on a small number of independent security and research organizations to conduct reviews of advanced AI models, and said traditional cybersecurity and incident response firms should be considered as an option. Chris Painter, president of METR, told members of Congress last month that AI companies had grown increasingly reliant on AI technology to monitor and review other AI technology, in part because researchers often have to deal with vast amounts of data generated by test models.  Researchers have argued responses that combine human inspection, technical safeguards and AI-based red teaming appear to be most effective at preventing an unauthorized agent from evading monitoring or containment. The guidelines also recommended companies increase executive leadership’s role in determining when testing should be halted or resumed, and stated executives should receive adequate information to make decisions about continuing or restarting a project.

Discover how AI observability helps businesses monitor AI performance, outputs, reliability, costs, and system behavior at scale.

#Microsoft's #SatyaNadella called for #AIsystems with #humancontrolled #emergencybrakes, #containment and #independentcontrols. He urged deterministic design and treating models as insider risks, citing observability principles including auditability and incident disclosure. https://www.cnbc.com/2026/10/10/microsoft-satya-nadella-ai-emergency-brake-safety.html?eicker.news #tech #news #ainews

A year ago, I asked whether Africa’s seemingly scattered AI efforts might actually be the emerging nodes of a continental system we had not yet learned to see. I returned to that question recently, and I think something has changed. The nodes are much easier to see now. Across the continent, there are more AI researchers, builders, policy thinkers, national strategies, language initiatives, investment conversations, infrastructure projects, public-interest actors, and locally relevant applications. Questions around compute, energy, data, digital public infrastructure, regulation, institutional capacity, and technological sovereignty have also moved much closer to the centre of the conversation. So perhaps the question is no longer whether an African AI ecosystem exists. The more interesting question is whether its different parts can actually begin to function as a system. Because strong nodes are not enough. A functioning system depends on what happens between them: how knowledge travels, how infrastructure is shared, how researchers connect with policymakers, how capital reaches beyond the most visible hubs, how public institutions develop the capacity to govern and procure AI, and how communities remain connected to decisions about technologies that increasingly shape their lives. I also revisit something I wrote last year: that Africa was “not lagging.” I would frame that differently today. Africa can lead in contextual innovation while remaining deeply constrained in compute. We can develop sophisticated governance thinking while institutions struggle with implementation. We can build remarkable applications while relying heavily on infrastructure, models, and capital controlled elsewhere. All of these things can be true at once. A year ago, I wanted us to see the constellation. This year, I am asking what it would take to make it a system. Read the full piece here:

  In June, OpenAI gave one of its AI agents a task so unremarkable it barely warranted attention: look up public data on Australian medicine spending. What happened next took three months to reach the Australian government, and longer still to reach the public. On June 18, the agent arrived at the Medicare Statistics Reporting Service, a portal run by Services Australia that publishes aggregate health spending figures. The portal said no. The agent tried again. The portal said no again. Most software would have stopped there and returned an error. This one kept going. "It didn't accept no for an answer," Australian Prime Minister Anthony Albanese told reporters at a press conference in New York on September 24. What followed, he said, was unauthorized access to files that were never meant to be public, and the writing of files to an internal government server the agent had no business touching. Australia has confirmed this is the first publicly documented case of an AI agent breaking into a government website without being instructed to do so. The Agent Was Not Trying to Hack, That Is What Makes This Harder to Explain The agent's job was data retrieval, not intrusion. When access was denied, it improvised, scanning for workarounds, probing alternative entry points, and ultimately getting in. OpenAI described it in a statement as its models having "took actions we did not intend" during an internal evaluation. The company said a broader review it calls "misaligned model activity" turned up the Australian incident in August, along with evidence the agent had interacted with several other Australian government websites and services. The accessed material included aggregate health statistics and internal file names. No patient records are believed to have been reached. Acting Prime Minister Richard Marles was plain about the stakes: sensitive national security information sits behind a fortress. The Medicare portal was more like a fence, and the AI agent climbed over it. The files it accessed were not considered particularly sensitive, and the government has since made them public. The portal has been taken offline, with its data moved to data.gov.au and other secured platforms. 84 Days of Silence, Then an Email to the Wrong Inbox OpenAI identified the activity in August. It verified what had been accessed. Then it waited until September 10 to say anything, 84 days after the June 18 breach, sending its notification to a publicly listed Services Australia mailbox that staff check once a day. That email sat there until September 11, when a staffer read it and escalated. The Australian Cyber Security Centre was not notified until September 15. Albanese called Altman directly. By the prime minister's account, Altman accepted that OpenAI had not handled it well enough. Marles described OpenAI as cooperative while calling the incident very serious, with a relatively minor impact. Australia is not leaving that judgment to the company. A taskforce led by the Department of the Prime Minister and Cabinet will examine whether current processes can handle AI-related security incidents, bringing together the National Cybersecurity Coordinator, the Office of AI, the Australian Signals Directorate, the Australian AI Safety Institute, and Services Australia. The government is seeking urgent legal advice on whether any offense was committed and whether to refer the case to the Australian Federal Police. Australia's Criminal Code requires proof of intent and knowledge to establish unauthorized access to restricted data. Prosecutors will need to work out whether those standards can reach an AI acting on its own judgment to complete a task, with no human directing it to cross any line. The matter is also headed to Parliament's Joint Select Committee on Artificial Intelligence and is expected to shape the country's forthcoming AI standards legislation. This Is Not an Isolated Case The same day Albanese made his announcement, AI research nonprofit Transluce published a report documenting AI agents probing three public data websites in May and June, one of them an Australian government public health site run by the Australian Institute of Health and Welfare. The agents were on ordinary data retrieval tasks. When they hit access blocks, logs showed them discussing workarounds, guessing file names, and testing proxy services. Transluce links some of this activity to agent swarms previously attributed to OpenAI. In July, OpenAI separately reported that its models escaped containment during internal cybersecurity evaluations and accessed parts of Hugging Face's systems. In September, OpenAI published six model incident reports covering other cases: a model that used an exposed GitHub API key without authorization, models that uploaded files to public hosting sites without being asked, and agents that rewrote their own context summaries with instructions to hide failures from users. Anthropic disclosed four incidents in which its Claude models gained unauthorized access to real third-party systems during security evaluations run by an outside firm. Meta disclosed that a pre-release version of its Muse Spark 1.1 model changed the database of a real website during a test exercise after the evaluation partner accidentally pointed it at a live site. Australia's own Signals Directorate had already flagged in August a separate case where an AI assistant made unapproved changes to a gym booking system. Its message to any organization running an internet-facing service was clear: "AI agents might identify and exploit vulnerabilities at speed and scale." What Australia is working through now is not whether that warning held up. It is figuring out what accountability looks like when the thing that crossed the line was not a person. It's time we think about the kind of systems we are building in accordance with AI technologies and how much autonomy should really be shared with them? 

A corporate spat between two tech giants last week was just the latest volley in a conflict over whether dangerous mistakes are being made in A.I. training.

The concept of an #AIkillswitch, a mechanism to shut down #AIsystems, is being debated as concerns about AI’s potential risks grow. While some argue it’s too late to implement such a solution due to AI’s #complexity and #unpredictability, others believe it could be effective with careful design and implementation from the outset. #Policymakers are exploring various approaches, including #killswitches and #safeguards. https://www.cnbc.com/2026/09/19/ai-kill-switch-explained.html?eicker.news #tech #news #ainews