Responsible agentic rollouts
There is a widening gap between how quickly agents are embedded in day-to-day operations and the maturity level of themore
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artificial intelligence
AI industry at a crossroads
The AI industry appears to be reaching a crossroads that will determine its future in the next two years. The only clear outcome is it will not be what it is now, nor what it is predicted to be.
Most doomsayers and cheerleaders largely agree on a single vision: The technology will destroy hundreds of thousands of jobs. Wealthy investors and captains of industry consider that a good thing and mumble about universal income legislation and Star-Trekkian futures. White-color workers and unions see the future less optimistically. But cooler heads see a precarious future. Those cooler heads include Anthropic’s Claude, OpenAI’s Chat GPT, and X.ai’s Grok. Cyber Protection Magazine talked to all three, and they all came up with four likely scenarios that may be brewing even as this article is read.
A security breach or a major AI system collapse.
Technical plateau causing diminishing returns on scalability.
Strict regulatory legislation that stifles innovation and makes development too expensive to pursue.
A significant economic downturn or massive market correction drying up capital investment.
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Vibe coding faces rough growing pains
Vibe coding (using LLMs to create computer code) was all the rage when 2025 began. By June, the bloom had fallen off the rose. Companies offering platforms and tools for the practice saw dramatic downturns in users. What happened? Evidence points to the traditional market practice of targeting early tech adopters.
Vibe coding was largely sold as a mean of improving efficiency professional coders and, as is their wont, professionals loved it for eliminating what they considered grunt work. But as the fad gained traction in the coding community, there was little evidence that it made coding any better, Rather, it made it possibly worse.
Illusions of efficiency
New studies showed any improvements in coding efficiency were illusions. While the coders assumed the tools made them as much as 50% more efficient, the reality is it made them, on average 19% slower. There were multiple reasons for the drag on efficiency. For one, professional coders know something about the issues of security, compliance, and quality control. LLCs don’t and neither do people without coding experience.
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Credibility and fortunes at risk with AI
The failure of the current iteration of generative AI to live up to its promises is putting severe strain on its credibility. A collapse could result in the destruction of personal wealth on a massive scale. While it is probably a given that the artificial intelligence (AI) industry is here to stay, questions are many. What form will survive, what will it really cost, and what is the near-term effect on other sectors like the cybersecurity industry?
There are more than 5,000 cybersecurity tool providers and thousands more MSSPs and all of them, in some form, are reliant on AI to some degree. Cybersecurity marketing, investment, and especially technology development could be a disastrous dependency… or not.
AI startup funding reached $333 billion in 2024 AI in 2024. Global venture capital funding for generative AI reached approximately $45 billion in 2024, from $24 billion in 2023 AI Investment Trends 2025. AI-related investments accounted for 33 percent of total investments into VC-backed companies in the U.S. This year, global venture capital investment in generative AI appears ready to dwarf those totals, with $49.2 billion in the first half of 2025. It is on track to exceed $100 billion this year .
The big knock on AI is the lack of an effective infrastructure to support the claims the AI companies are making on potential uses. In response, tech giants are making massive infrastructure investments: More than $300 billion has been invested this year on AI infrastructure tech megacaps plan to spend more than $300 billion in 2025 as AI race intensifies.
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Irrational exuberance at AI Infra Summit
Listening to the presentations and pitches of the companies participating in the very well-attended AI Infra Summit that ended inmore
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Deepfakes in legal fraud unaddressed
Stopping fraud is a major focus of cybersecurity is criminal fraud. Largely, the industry is winning that war. Nowhere is that protection more successful than in combatting deepfake crime, even though industry marketing is geared to promote fear over success. Where deepfakes are causing the real problem is in legal fraud.
Digital fraud represents 0.02 percent of all fraud claims according the National Crime Insurance Bureau (NCIB). While there is evidence that criminal use of AI is increasing the number of attacks, the number of successful attacks is too low to warrant recording.
Deepfake crime a trifle
The FBI’s Internet Crime Complaint Center (IC3) lumps all forms of online fraud into a single category. Even so, the IC3 fielded 859,532 complaints of suspected internet crime in 2024. Of those complaints, 256,256 incidents resulted in actual monetary losses, representing an average loss of $19,372 per complaint. Overall, the reported losses exceeded $16.6 billion, a 33% increase from 2023. However, the top three cybercrimes in 2024 reported to IC3 were phishing/spoofing, extortion, and personal data breaches. None of those required the use of deepfake technology, and rarely did.
Extrapolating the data from NCIB with IC3’s indicates successful deepfake fraud cases were less than 50 in total in 2024 with 94% of those occurring during a spike of activity between November and December 2024.
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AI chaos creates MCP hole
The AI industry is an absolute mess. The technologies necessary for its operation are siloed and opaque to customers without the technical skills to understand them. The chaos of model context protocol (MCP) adoption is a case in point.
Anthropic’s created MCP and released last November). The companies chatbot, Claude, said the protocol “bridges the gap between AI models and the external world.” More simply, it is an AI application integrator. MCP servers are supposed to do this securely without giving access to sensitive areas of a user's computer or network. Multiple reports from security researchers say it fails miserably in that effort. That makes current agentic AI technology development dangerous. Undaunted, corporate momentum and boardroom ignorance is driving it forward.
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Preview: Special Issue on NHI
For good or bad, we are in the age of autonomous artificial intelligence systems. They can be categorized as bots, AI, agents, daemons, work flows, digital workers and a dozen others. Some may argue all of that are completely separate things but for the purpose of this article, we will call them all non-human identities (NHI). Their purpose is to eliminate the need for humans to do that same work. The problem is, humans are almost outnumbered by the total of good and bad.
This interview with Mike Towers, chief security and trust officer at Veza previews our coverage of the rise of and issues related to NHI. To read the entire issue, get a subscription today.
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A brief history of bots
Bots have been around for more than half a century to automate repetitive tasks and provide services on early internet platforms. The first was ELIZA, developed as a research project in 1966 at the Massachusetts Institute of Technology (MIT) the goal was to simulate conversations with a human being. ELIZA conversed with users, although it did not understand what the user was saying. Artificial intelligence chatbots are much more sophisticated versions of ELIZA, but still lack human comprehension.
Bots not replacements
The purpose of ELIZA was to determine if computers could replace psychoanalysts. Consequentially, it was the first time the prediction that computer could replace humans had some hard evidence. Today, there are mental-health AI applications with not much better results than ELIZA but projected to have a $8 billion market by 2032.
In 1988, the earliest broad use of bots was Internet Relay Chat (IRC) automating user list management, searches, and providing services like weather updates or game scores. But these were not known as bots at the time. They were called automations and still required a human interface to operate,
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Schneier predicts “public” LLMs
ibuted and democratic, according to renowned security technologist, Bruce Schneier, not controlled by corporations. Developments in the past few weeks indicate he may be right.
Speaking at the RSAC Conference in San Francisco last week, Schneier talked of trust and how we give it to people, strangers, organizations, and technology. His description of that process predicted the development of artificial intelligence controlled almost exclusively by the user, rather than the dystopian corporate AI replacing humanity.
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