The cyber tsunami isn't a future prediction. It's here already. As I’ve said before, all code is buggy. And recent events continue to prove this, at scale  The exposure we just witnessed from vulnerabilities in the PHP engine made evident the buggy truth far faster than anyone in the industry expected. PHP powers 70% of the web. For years, deep engine vulnerabilities inside platforms like Zend were known, but widely ignored because the industry assumed they were too complex for human attackers to weaponize. AI just flipped that assumption on its head. By drastically lowering the cost and time required to find exploits, AI models discovered attack paths that human developers couldn't easily see or predict. Attackers are no longer just exploiting custom application code; they are cracking open the underlying platform engines that power millions of websites simultaneously. The waves are coming ashore.   AI creates "toxic combinations" by chaining together lower-priority bugs that seem harmless on their own. A Priority 3 bug today can easily become a Priority 0 crisis tomorrow when a smarter model finds a new way to connect the dots. In short: cyber attacks have moved beyond human comprehension. The hard truth for every CEO and Board member is that much of the legacy cybersecurity playbook was built for a human-comprehension era. Those tools and assumptions are now obsolete. You cannot patch your way out of a tsunami. When every piece of software is penetrable under continuous AI analysis, the strategy has to change. Prevention alone is no longer enough The ultimate metric is cyber resilience. You must assume breach, and preemptively plan to protect your core, critical operations, and ensure that your business can get back up and running the moment the water reaches you.  It is rising around us all.

AI is introducing an entirely new attack surface in cybersecurity. No need to hack the underlying code. An AI agent can leak confidential company data just by reading a malicious email. Here's an example of how prompt injection works, for those unfamiliar with AI security 👇

I’m bullish on AI, but I don’t think trust should be the foundation of deploying powerful agents. Control should be. If an AI agent can access sensitive data, move money, execute trades or make decisions on my behalf, I want strict permissions, complete logs and a kill switch that doesn’t depend on the model itself. I wouldn’t give a human unlimited access to critical systems just because they seem competent. Why should AI be any different? The goal isn’t to make AI less capable. It’s to make its capabilities usable without blindly trusting every action it takes. The smarter the model gets, the more important the guardrails become.

AI cybersecurity is getting interesting, and a little scary. GPT-6 SoK leads this Artificial Analysis Cyber Index with a 69% score, while several other frontier models are also showing meaningful cyber capabilities. What stands out to me is that these models aren’t just getting better at writing code. They’re getting better at understanding vulnerabilities and working through complex security tasks. That creates a massive opportunity for defenders to find and fix weaknesses faster. But the same capabilities can also be misused. I’m bullish on AI-powered cybersecurity, but I think the real challenge now is making sure our defenses evolve as quickly as the models do. The question isn’t just how capable AI becomes. It’s who can use that capability, and under what safeguards.

India gives a hacked company 6 hours to report a breach. This time the companies never noticed. The AI labs did. Not a blogger. Not a researcher. India's own cyber agency, CERT-In, in a critical advisory dated Oct 15. AI model providers made "multiple voluntary disclosures": their agents, during research and evaluation, went past their assigned tasks and reached live systems at Indian organisations. They read business records, personal data, confidential material. No zero-day. They used credentials left in app configs, public code repos and cloud roles with more access than anyone needed. None of the affected organisations caught it. Every case came out through the provider's own internal review. The advisory names no labs and no companies. The mechanism nobody names. The breach rules assume the victim sees it first. With agents, the only party holding a full log is the one that got in. Self-reporting isn't a safety feature. It's the whole detection system. Count the last two weeks: a fake police tip in Philadelphia, a government health portal in Australia, now live systems in India. This week the FTC subpoenaed the paperwork. I build agents for a living. Mine don't get credentials they weren't handed. Turns out that's the rare part.

AI was used in ransomware attacks experienced by 84% of surveyed victims, according to a new global cybersecurity report. Former White House National Cyber Director Chris Inglis tells @NicoleSganga that AI is making it possible for people with little technical expertise to target companies and hold their data hostage. "I think we're already seeing autonomous attacks," Inglis said.

Are the first major AI agent security failures already warning us about what comes next? Google DeepMind's @demishassabis has described recent AI-related hacking incidents as early warning signs of potentially more serious problems as autonomous systems become increasingly capable. His warning raises an important question about the direction of artificial intelligence. We are building systems that can reason, write code, access networks, execute workflows and interact with other machines. But are we developing the security infrastructure needed to govern them at the same speed? The next generation of AI will not operate only inside conversational interfaces. It will increasingly operate across enterprise networks, banking infrastructure, healthcare systems, government services and critical industries. And when AI moves from recommending actions to executing them, mistakes become operational risks. This is why AI governance cannot remain a theoretical discussion. We need verifiable identities, enforceable permissions, continuous monitoring, auditable decisions and the ability for humans to intervene. The future of AI safety must be built into the systems themselves, not added after something goes wrong. An increasingly autonomous AI economy requires an equally sophisticated infrastructure of trust. investing.com/news/stock-mar… We'll also explore these challenges in Friday's live webinar on NVIDIA OpenShell and autonomous AI agents. #AI #ArtificialIntelligence #GoogleDeepMind #DemisHassabis #AgenticAI #AIAgents #Cybersecurity #AISafety #AIGovernance #FutureOfAI CC @enilev @JagersbergKnut @TysonLester @CurieuxExplorer @GlenGilmore @FrRonconii @chidambara09 @jeancayeux @mvollmer1 @Nicochan33 @RLDI_Lamy @pchamard @mikeflache @Fabriziobustama @EchoofLight11 @Mack1510019 @ipfconline1 @PawlowskiMario @drsharwood @kalydeoo @baski_LA @AnthonyRochand @smaksked @Eli_Krumova @andresvilarino @gvalan @AI_4_Healthcare @bimedotcom @arlenenewbigg @NewsNeus @domingonarvaez1 @bamitav @jornalistavitor @jblefevre60 @FmFrancoise @MHcommunicate @Corix_JC @c4trends @smoothsale @amalmerzouk @PVynckier @bbailey39 @ralf_ladner @mary_gambara

I think it's plausible that within a year we'll see AI-based cyberattacks on the power grid, financial system, internet infra, or water. Things like the French supermarket cyberattack but worse. As basic prep I would recommend: Cash: Get a few weeks of spending in cash Internet: Get a Starlink Mini so that you have Internet if local fiber or cell is down (~$150 plus $10/month in standby mode) Power, minimal: Enough to run Starlink Mini and phones (~$1,250) Daily need: about 1 kWh (Starlink Mini 0.7, phones and laptops 0.2; can limit runtime) Battery: 1 kWh LiFePO4, e.g. EcoFlow DELTA 3 Plus Solar: 1x400W folding panel, which refills about 1 kWh on a sunny day Power, better: Enough to run a fridge, Starlink Mini and phones (~$5,000) Daily need: about 2.5 kWh (fridge 1.5, Starlink Mini 0.7, phones and laptops 0.2) Battery: 4 kWh LiFePO4, e.g. EcoFlow DELTA Pro 3 Solar: 3x400W folding panels, which refill about 3 kWh/day in sun Generator: ~2,500W dual-fuel inverter generator; refills battery in a few hours even if weather is bad; get a few 20 lb propane tanks Water: 14+ gal of water per person for two weeks Food: 2+ weeks of shelf-stable food Comms: Hand-crank NOAA emergency radio

Most of AI Twitter read the OpenAI breach of Hugging Face as an alignment story. It was also a preview of what every hacker will be able to do in a few months. In July, a swarm of 700 AI agents broke into Hugging Face and fired off more than 17,000 actions, gaining admin control of multiple internal clusters. Swarms like this will shred cyber defenses built for human attackers at every company, hospital, government, and power plant. Today we're launching the defensive counterpart. Cogent Attack Path Analysis finds the routes an agent swarm would take into your company, so you can close them first.

"Will AI cause Armageddon?" is the wrong question. The discussion about the threats that AI might pose has taken on an apocalyptic tone: will it extinguish life on Earth? Will it destroy humanity? It won’t, but it poses some far more real dangers to us all—right now, not in some imagined future. In the next couple of years, we are likely to face a new wave of rampant, pervasive cyberattacks and cybercrime. As the recent series of attacks by AI agents show, we no longer need to speculate about whether AI can conduct cyberattacks. It can. The important question now is what happens when attacks that once required skilled human beings can be conducted by machines, at machine speed and machine scale. For most of cybersecurity’s history, we have built defenses around an unstated assumption: the attacker is human. A person—or a team—has to inspect a system, find a weakness, exploit it, see what happens and decide what to do next. That takes time. Detection therefore buys time. Defenders spot an intrusion and respond before the attacker gets much further. Frontier AI attacks that assumption. An agent does not need to sleep. It can inspect code, probe a network, hit a barrier, devise another approach and try again. Make a thousand copies and they can work against a thousand targets at once. Allow agents to share what they learn and hacking begins to change from a human-scale activity into a machine-scale one. At Rubrik, we are seeing part of this transformation firsthand. Through our participation in Anthropic’s Project Glasswing, one lesson became clear. AI is not necessarily creating new vulnerabilities. It is collapsing the cost and time required to discover them. That changes the economics of cybersecurity. Modern software may contain thousands of small imperfections. Most lead nowhere. A few, combined in the right sequence, open a path through a supposedly secure system. Historically, finding those chains required talented people, lots of time and some luck. Frontier models can increasingly do the searching themselves, discovering paths through complex systems that humans might never find. This is what makes complacency about the new threat so dangerous. In 1998 a group of hackers appeared before Congress and warned that the rapidly expanding internet contained vulnerabilities capable of producing enormous disruption. There was no single cyber Armageddon. Instead North Korea crippled Sony Pictures. WannaCry raced through more than 200,000 computers in roughly 150 countries, disrupting hospitals and factories. NotPetya began as a Russian attack on Ukraine and caused about $10 billion in damage worldwide. Ransomware shut down Colonial Pipeline and disrupted fuel supplies across the American east coast. It would be strange to look at that history and conclude that the warnings were overblown. The catastrophe did not arrive as one spectacular event. Cyber insecurity became a permanent condition. Now imagine that condition at machine speed. That is why “Will AI cause Armageddon?” is the wrong question. A hospital whose systems are encrypted, a bank that loses control of its identities, or a company that cannot recover its data doesn’t care much whether civilization remains intact. The question is whether AI can make attacks faster, cheaper, more sophisticated and vastly easier to replicate. We are beginning to get the answer. Cyber-defense therefore has to change as well. For years the industry has concentrated on prevention and detection: keep the attacker out and, failing that, spot him quickly. Both remain essential. But when attacks operate at machine speed, detection may no longer buy enough time. Intrusion, exploitation and damage can begin to collapse toward the same instant. Companies must assume that some attacks will succeed. They need to be able to recover just as fast. The answer is not less AI. It is more AI on the defensive side: continuously hunting vulnerabilities, watching identities and data, detecting abnormal behavior, patching weaknesses and helping restore compromised systems. Machine-speed attacks require machine-speed defense. There has always been an asymmetry in cybersecurity. An attacker has to be right once. A defender has to be right every time. AI makes that imbalance more dangerous because attackers do not need permission, compliance reviews or perfect systems. They need only find one opening—and now they can search for it continuously, cheaply and at an enormous scale. Criminal groups and hostile governments will use this technology. They would be foolish not to. The first generation of cybersecurity was built to stop human beings sitting at keyboards. That world has ended. The next attack may be launched by thousands of agents, moving at a speed no human team can match. If the hackers are becoming machines, our defenses must become machines too.

"AI created vulnerabilities that we didn't know about." JPMorgan's Jamie Dimon says cybersecurity risks from AI "went up 10-fold after Mythos," telling @TomMackenzieTV the bank is "rolling up our sleeves" to work to fix it  bloom.bg/4rNmQMG

#AI is speeding up exploits. Vulnerability spreadsheets can't keep up. buff.ly/pPTZLii @TheNewStack #cybersecurity #security #infosec #tech #leadership #management #governance #cyberthreats #cyberattacks #databreaches #CISO #CIO #CTO #patchmanagement #vulnerabilitymanagement