Podcasts

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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Cybersecurity companies underinsured?

Data breaches are a major concern to businesses and governments around the world. So one would think that carrying cyber insurance would be a given. It is not, especially for one particular classification of industry: Cybersecurity.

According to Munich Re, a risk analysis firm, 87% of companies lack coverage. Ransomware payouts doubled to $1.1B in 2023, according to Chainalysis. That’s probably why the cyber insurance industry is booming. The market hit $14B in 2023 and is set to double to $29B by 2027.

Large firms are more likely to carry insurance than small to medium companies (SMCs), even though they are more likely to be targeted by cybercriminals. However, small companies are more likely to carry much larger limits than larger companies.

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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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Zero Trust: easy concept, hard to implement

Last week, Dr. Zero Trust, AKA Dr. Chase Cunningham, posted in Linkedin that he was fed up with people who say they don’t understand Zero Trust. To a certain extent, I feel his frustration.
Journalists understand the concept. We have a decades-old saying, “If your mother says she loves you, check it out.” It doesn’t get more zero trust than that.
The problem is that while it’s easy to understand as a concept, it isn’t easy to build a zero trust infrastructure, especially with the misleading gobbledygook most cybersecurity companies put out. Cunningham says there are hundred of books and articles on the subject. He’s right, of course. The question is, which one do you choose?
At the RSAC Conference, We sat down and briefly talked with Dale Hoak, CISO for RegScale, about how easy it is to understand Zero Trust

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An encryption primer: Don’t wait

Encryption became a hot topic in the news in the past month. The United Kingdom, Sweden, France and the EU are considering requiring “back doors” to encryption protections. The “Signalgate” scandal in Washington, DC started most people asking, “What is this encryption stuff?” So we decided to provide a primer on the state of encryption today.

While the technology behind encryption is complex, it is not new. The basic algorithms have been with us for decades, silently running on devices and servers, invisible to the user. The purpose is basic: to keep data safe from prying eyes, like criminals and nation states.

Encryption is also a good way of saving money and not just in avoiding ransoms. Insurance companies often offer up to 15% premium discounts to businesses demonstrating strong security practices, including proper data encryption. Encryption significantly reduces the risk of data breaches and their associated costs.

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AI bubble about to pop for cybersecurity?

As quickly as the artificial intelligence (AI) industry appeared, it may disappear just as quickly. That may have significant ramifications for cybersecurity, according to industry watchers, as the technology falls into the trough of disillusionment.

When OpenAI burst on the scene more than two years ago, Microsoft was a significant instigator in its growth and adoption. Microsoft invested billions in the not-for-profit enterprise for early access to cutting-edge AI technologies and helping accelerate OpenAI's research. It transformed its Azure cloud platform into a leading infrastructure provider for AI development, offering specialized hardware (like GPUs and TPUs) and services tailored for machine learning workloads. AI capabilities were embedded across its product suite, and Microsoft Research contributed significantly to AI advancement in computer vision, natural language processing, and deep learning.

All of that came with extreme demands on computing resources. Microsoft began a buying spree in data centers, both to secure resources and build new centers. They even entered into a deal to reopen the notorious Three Mile Island nuclear power plant.

Spree ends

That has all come to an end. As reported in Bloomberg last week, the company decided to scale back data center projects in the UK, Australia, and Indonesia. Data center development in Illinois, North Dakota, and Wisconsin is also canceled. All tolled, Microsoft has walked away from more than 2GW. That’s on top of the news that Microsoft had walked away from two data center projects in the US and Europe, piling on to a February announcement that it was canceling data center leases.

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