For decades, the industrial sector has operated on the simple mantra to live by automation, die by automation. In the oil and gas industry, where precision is measured in millimeters and safety in lives, automation is a necessity, not just nice to have. But as gen AI sweeps through the enterprise, a new challenge has emerged in how a global leader in energy services should transition from experimental chatbots to industrial-grade AI without compromising safety or security.
Here, Alex Philips, CIO of NOV, formerly National Oilwell Varco, discusses implementing OpenAI and securing it with zero trust for 25,000 employees, and why the next phase of agentic AI requires a fundamental shift in how to view human expertise and digital safeguards.
Like many global companies, NOV’s initial move into gen AI was driven by executive pressure fueled by fear of missing out. Philips remembers the early talks with his CEO about the investment.
“I said we have this opportunity, and it costs this much,” he says. “He asked about the ROI and I replied that’s something I couldn’t calculate, nor what it’d replace or what it’d displace in cost, but I couldn’t say any of that for email either.”
Just as no modern business can function without email, even without a direct line-item ROI, Philips argues that LLMs will soon become the standard for employee productivity. Currently, NOV reports about 50% of its workforce actively use the tool to enhance productivity.
The results, though qualitative, are profound. Philips says that response times for urgent customer requests, for instance, have plummeted, language barriers are crumbling, and employees are tackling complex analyses once considered out of reach.
One example Philips details involves an engineer who spent six months mastering a highly specialized skill. With ChatGPT, the engineer was able to replicate that six-month learning process in just 10 minutes.
And while his initial response was to think he wasted six months of his life, the response was to show him he spent six months to validate what the AI told him. “This is a great example of why humans are still needed in the AI loop,” says Philips. “AI execution without human validation can lead to errors that cost companies significant time and money.”
This underscores the crucial pillar of NOV’s AI strategy of human accountability because in an industrial setting, AI dictating terms is never an acceptable excuse. Whether designing a drill bit or automating a workflow, the end user remains responsible for the output.
As AI becomes more widespread, shadow AI poses a significant security risk. To address this, NOV uses Zscaler to route all traffic, and ensure visibility and control. And by doing so, the company can:
In software development, NOV already benefits from AI-assisted coding, where AI works alongside developers who accept about 32% of AI suggestions. “We’re now beginning to explore the next evolution of full agentic coding,” says Philips, adding that this next stage truly supercharges teams, enabling them to move faster and better meet customer demand for innovation.
However, this efficiency feeds the dilemma of a widening talent gap. The challenge moving forward is if all the low-level, entry-level tasks can be automated, and what’s the best way to develop skilled workers. “I don’t know how we’ll adapt to it, but we’ll figure it out,” he says.
In the oil field, some processes are too critical to be left entirely to a black-box algorithm. Philips is adamant that for safety issues, AI remains an advisor, not a decider. NOV uses AI-powered vision to monitor red zones, or dangerous areas on a drilling rig. If the AI detects a person in a restricted area, it can trigger an emergency stop. However, for actual drilling operations, the final call remains with an onsite human operator. “You can’t have a hallucination,” he says. “You can’t say it’s right 90% of the time. It has to be all the time.”
NOV’s journey shows that transitioning to industrial-grade AI isn’t just about choosing the best model but building a framework of trust, transparency, and responsibility. By using Zscaler for governance and GitHub Advanced Security for code validation, NOV is moving toward a future where AI becomes more essential to the oil industry.
“Development teams should produce twice the output with half the people in half the time,” he says. “The only remaining question is how do we train the next generation of developer experts to control the machines that do the work.”
The value of Bitcoin has had its ups and downs since its inception in 2013, but its recent skyrocket in value has created renewed interest in this virtual currency. The rapid growth of this alternative currency has dominated headlines and ignited a cryptocurrency boom that has consumers everywhere wondering how to get a slice of the Bitcoin pie. For those who want to join the craze without trading traditional currencies like U.S. dollars (i.e., fiat currency), a process called Bitcoin mining is an entry point. However, Bitcoin mining poses a number of security risks that you need to know.
Mining for Bitcoin is like mining for gold—you put in the work and you get your reward. But instead of back-breaking labor, you earn the currency with your time and computer processing power. Miners, as they are called, essentially maintain and secure Bitcoin’s decentralized accounting system. Bitcoin transactions are recorded in a digital ledger called a blockchain. Bitcoin miners update the ledger by downloading a special piece of software that allows them to verify and collect new transactions. Then, they must solve a mathematical puzzle to secure access to add a block of transactions to the chain. In return, they earn Bitcoins, as well as a transaction fee.
As the digital currency has matured, Bitcoin mining has become more challenging. In the beginning, a Bitcoin user could mine on their home computer and earn a good amount of the digital currency, but these days the math problems have become so complicated that it requires a lot of expensive computing power. This is where the risks come in. Since miners need an increasing amount of computer power to earn Bitcoin, some have started compromising public Wi-Fi networks so they can access users’ devices.
One example of this security breach happened at a coffee shop in Buenos Aires, which was infected with malware that caused a 10-second delay when logging in to the cafe’s Wi-Fi network. The malware authors used this time delay to access the users’ laptops for mining. In addition to public Wi-Fi networks, millions of websites are being compromised to access users’ devices for mining. When an attacker loads mining software onto devices without the owner’s permission, it’s called a cryptocurrency mining encounter or cryptojacking.
It’s estimated that 50 out of every 100,000 devices have encountered a cryptocurrency miner. Cryptojacking is a widespread problem and can slow down your device; though, that’s not the worst that can happen. Utility costs are also likely to go through the roof. A device that is cryptojacked could have 100 percent of its resources used for mining, causing the device to overheat, essentially destroying it.
Now that you know a little about mining and the Bitcoin security risks associated with it, here are some tips to keep your devices safe as you monitor the cryptocurrency market:
The post Bitcoin Security: Mining Threats You Need to Know appeared first on McAfee Blog.
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