The Forescout 2026 H1 Threat Review found that more than 37,000 vulnerabilities were published during the first six months of the year, representing a 51% increase year on year. More than half were classified as high or critical severity, while ransomware attack claims rose by 25% to 4,544 incidents, averaging 25 attacks every day.

The report, published by Forescout Research – Vedere Labs, analysed more than 37,000 vulnerabilities, over 1,000 tracked threat actors and thousands of cyberattacks observed between January and June 2026. Researchers found that rapid advances in AI, alongside growing geopolitical tensions, are increasing the pressure on security teams already struggling to prioritise risk.

Among the report‘s key findings, researchers discovered that nearly half of all additions to CISA’s Known Exploited Vulnerabilities (KEV) catalogue related to vulnerabilities published before 2026, reinforcing the continued risk posed by older, unpatched flaws. The number of active ransomware groups also increased to 103, while China, Russia and Iran collectively accounted for almost a third of tracked threat actors with significant activity during the reporting period.

The research also highlights the growing use of AI by threat actors to accelerate attacks, alongside increasingly sophisticated software supply chain compromises. At the same time, attackers continue to focus on network infrastructure, operational technology, IoT and IoMT devices, many of which receive less security oversight than traditional endpoints.

“AI is dramatically increasing the speed and scale of cyberattacks,” said Daniel dos Santos, VP of Research at Forescout.

“In observing attack patterns and threat actor activity, we can see that AI is helping threat actors discover and exploit vulnerabilities faster than security teams can realistically remediate them. At the same time, geopolitical conflicts are fuelling waves of opportunistic and state-aligned cyber activity, with organisations in critical infrastructure sectors increasingly at risk.”

He added that organisations need a better understanding of the assets connected to their networks so they can prioritise risk and contain threats before attackers can move laterally into critical systems.

The report also examines the evolution of Iranian cyber operations, noting that the distinction between state-sponsored actors, hacktivist groups and cybercriminal organisations is becoming increasingly blurred. Researchers found these groups are using a mix of espionage campaigns, ransomware and attacks targeting critical infrastructure and operational technology.

Barry Mainz, CEO of Forescout, said organisations must extend their focus beyond traditional endpoints to address unmanaged assets and connected devices.

“As attack surfaces continue to expand, security teams can no longer focus exclusively on traditional endpoints,” he said.

“Many organisations still have significant blind spots across unmanaged assets and IoT, OT, and IoMT devices. Threat actors understand this and are increasingly exploiting those gaps.”

The report recommends that organisations should continuously identify vulnerable assets, strengthen network segmentation, prioritise the highest-risk systems and accelerate response capabilities to reduce exposure across increasingly complex environments.

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I always end up with more pulled pork than I know what to do with after a smoke, and this is my go-to way to use it up. Melty cheese and smoky pork between two crispy slices of buttered sourdough is hard to beat, and it comes together in about the time it takes to […]

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One of the most exciting moments for beginners entering cybersecurity is discovering ethical hacking tools. You install your first security-focused operating system, open a terminal, explore different applications and suddenly feel like you’ve entered a completely new world. However, there is one important lesson every beginner should understand: Tools do not make someone an ethical […]

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(Video) Artificial intelligence is transforming cybersecurity, but are governance, compliance, and security practices evolving fast enough to keep up?

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The EU AI Act’s security requirements go beyond governance documentation and AI literacy training. High-risk AI systems need adversarial testing to prove they can withstand real attacks. Policies describe intent. Testing produces evidence. Security teams that add structured adversarial testing to their AI compliance programs will have something governance documentation alone cannot produce: demonstrated assurance. […]

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Large XRP whales have accumulated more than 4 billion XRP tokens since the ongoing downtrend began in July 2025. While the current downward price action has dampened market sentiment, leading to panic among retail investors, market data suggests that large whales have instead taken advantage of the lower prices to load up on their bags.

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Many beginners start learning ethical hacking with excitement. They install security tools, watch tutorials, create accounts on learning platforms and imagine themselves finding vulnerabilities like professional security researchers. But after a few weeks, reality often feels different. Suddenly there are hundreds of concepts: What looked exciting from the outside begins to feel confusing. Many beginners […]

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Bread Pakora is one of the most delicious rainy day snacks. It is traditionally deep fried, making it quite heavy on oily. But in this version, we make bread pakoras in the air fryer using a fraction of the oil. Which means you can enjoy them guilt-free! 

Bread pakora used to be a monsoon staple in my house growing up and it’s a dish that’s filled with nostalgia for me. But of course as I grew up, the guilt of eating the traditional deep-fried version started to show up. Until I decided to experiment and learn how to make the same bread pakoda in my trust-y air fryer 😍

This air fryer bread pakora has got the same light and crispy batter, but made with just a tablespoon of oil. Which means you get to enjoy your favorite rainy day snack without any guilt! And believe me, no one would be able to tell the difference! 

Bread Pakora Ingredients 

Batter: 

• Besan: Aka gram flour. This forms the base of the batter, adds a nutty flavor, and helps crisp up the bread pakoras. 
• Onions: Thinly sliced. Adds a slight crunch and a boost of flavor
• Carrom seeds: Adds a lovely aroma and a slight peppery flavor. Highly recommend not skipping this.    
• Oil: Any neutral-flavored cooking oil like peanut, sunflower, or vegetable. 
• Coriander leaves: For an earthy freshness
• Water: To adjust the consistency of the batter
• Salt: For seasoning

Filling:

• Potato: Boiled, peeled, and mashed.   
• Spices: Green chilli, jeera powder, chaat masala, turmeric powder, and Kashmiri red chilli powder for flavor and heat. 
• Ginger: Grated ginger adds a lovely heat and aroma   
• Salt: To season

Serve: 

• Bread: I have used white bread, but brown bread works too.  
• Chutney: To serve along with the bread pakoras. I love green coriander chutney with this. 

Frequently Asked Questions 

Richa’s Top Tips 

• Let the batter rest: Resting the besan batter for about 10 minutes helps the flour hydrate properly, resulting in a smoother coating that sticks better to the bread.
• Don’t make the batter too thin: A semi-thick, pourable batter gives the best coating. If it’s too runny, it will slide off the bread and won’t crisp up as well.
• Spread the filling evenly: A thin, even layer of the potato mixture prevents the sandwich from falling apart while dipping and air frying.
• Press the sandwich gently: This helps seal the bread before coating it in batter, making the bread pakora recipe much easier to handle.
• Serve immediately: Like most pakoras, these taste best straight out of the air fryer while the coating is still crisp.

Storage Tips

Bread pakoras are at their crispiest when served fresh, but leftovers can still be enjoyed later.

• Refrigerate: Store cooled pakoras in an airtight container in the refrigerator for up to 2 days.
• Reheat: Air fry at 180°C for 3-5 minutes until hot and crisp again. Avoid microwaving, as it softens the coating.
• Freeze: You can freeze the assembled, uncooked sandwiches (without the batter) for up to 1 month. Thaw slightly, dip in fresh batter, and air fry as directed. This is a great shortcut if you love making bread bajji for quick snacks.

Serving Ideas

• These crispy bread pakoras are a staple in our house as an evening snack with a hot cup of masala chai or ginger tea, especially when it’s raining. 
• Traditionally, these are served with green chutney, tamarind chutney, or tomato ketchup. When I am hosting, I simply arrange all three so everyone can mix and match.
• For a more filling meal, pair your bread pakora recipe with a bowl of hot tomato soup or simple vegetable soup. 

Make these Bread Pakoras Your Own

• Add grated paneer or crumbled feta to the potato filling for a richer, creamier centre.
• Mix finely chopped vegetables like carrots, capsicum, peas, or sweet corn into the filling for extra texture and nutrition.
• Add grated cheese for a gooey, kid-friendly version.
• Make it spicier by adding more green chillies, crushed black pepper, or red chilli flakes.

Conclusion

If you’ve been wondering how to make bread pakoras that are crispy, satisfying, and don’t require deep frying, this air fryer version is one you’ll come back to again and again. Perfect for rainy days, evening tea, or whenever you’re craving a comforting snack, this recipe proves you don’t need lots of oil to get delicious results. 

If you make this recipe, DM me your pictures or tag me in stories over on my IG @my_foodstory ❤️  

Watch Air Fryer Bread Pakoda Recipe Video

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Air Fryer Bread Pakora

Crispy, crunchy, and super flavorful just like it's deep-fried version, these air fryer bread pakoras are healthy and perfect way to eat your favorite monsoon snack guilt-free!

Course Snacks & Appetisers

Cuisine Indian

Diet Vegan, Vegetarian

Prep Time 10 minutes minutes

Cook Time 15 minutes minutes

Total Time 25 minutes minutes

Servings 2 pakoras

Calories 479kcal

Author Richa

Equipment

• 1 Air Fryer

Ingredients

For batter

• ½ cup + 2 tablespoons gram flour / besan
• ¼ teaspoon salt
• ¾ onion thinly sliced, approx. ⅓ cup + 2 tablespoons
• 2 teaspoons finely chopped coriander leaves
• A pinch of carom seeds ajwain
• 1 teaspoon oil
• ¼ cup + 1 teaspoon water

For filling

• 2 boiled potatoes peeled and mashed, approx.⅓ cup
• 1 green chilli finely chopped
• ½ teaspoon grated ginger
• ⅛ teaspoon salt
• ⅛ teaspoon chat masala
• ⅛ teaspoon jeera powder
• ⅛ teaspoon turmeric powder
• ⅛ teaspoon kashmiri chilli powder

To serve

• 4 bread slices
• 2-3 tablespoons green chutney

Instructions

Preparing batter

• Take gram flour, salt, onions, coriander leaves, ajwain in a bowl & mix well. Add oil, water and mix to make a semi-thick batter. The batter should be of pouring consistency but not runny. Set aside for 10 minutes
  ½ cup + 2 tablespoons gram flour / besan, ¼ teaspoon salt, ¾ onion, 2 teaspoons finely chopped coriander leaves, A pinch of carom seeds, 1 teaspoon oil, ¼ cup + 1 teaspoon water

Preparing filling

• Take potatoes in another bowl, add rest of the ingredients for the filling, mix well and set aside.
  2 boiled potatoes, 1 green chilli, ½ teaspoon grated ginger, ⅛ teaspoon salt, ⅛ teaspoon chat masala, ⅛ teaspoon jeera powder, ⅛ teaspoon turmeric powder, ⅛ teaspoon kashmiri chilli powder

Assembling pakodas

• Place both the bread slices on a plate or chopping board, spread chutney on one of the slices. add approx. ¼ cup of the potato filling on the other slice and spread well. Cover with the chutney spread slice to form a sandwich. Press gently and cut them into 2 triangles. Repeat the same process for the rest of the bread slices.
  4 bread slices, 2-3 tablespoons green chutney
• Pre heat the air fryer at 180C and brush the basket with oil.

Preparing pakodas

• Dip each triangle sandwich in the batter, ensuring they are evenly coated. Place on the air fryer basket, spray/brush with a few drops of oil and air fry at 180 C for 8 minutes, flip the pakodas, spray/brush with a few drops of oil and air fry at 180 C for 5 minutes till they are roasted well.

Video

Nutrition

Calories: 479kcal | Carbohydrates: 87g | Protein: 18g | Fat: 7g | Saturated Fat: 1g | Polyunsaturated Fat: 2g | Monounsaturated Fat: 2g | Trans Fat: 0.03g | Sodium: 814mg | Potassium: 1300mg | Fiber: 12g | Sugar: 11g | Vitamin A: 64IU | Vitamin C: 48mg | Calcium: 122mg | Iron: 5mg

This article was researched and written by Urvi Dalal.

The post Air Fryer Bread Pakora appeared first on My Food Story.

Chart data identifies an area that may represent the most reasonable zone for XRP to find its bottom in the ongoing bear market cycle. The current downtrend has continued to weaken investor sentiment, as XRP records some of its biggest losses in recent times.

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Following its longest hibernation period ever of nearly a year, NASA’s New Horizons spacecraft has emerged in good health and is ready to begin transmitting science data gathered in the distant Kuiper Belt far beyond Pluto.

On June 23, flight controllers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, confirmed New Horizons, acting on stored commands uplinked to its main computer last July, had safely awakened from a 321‑day hibernation period that began Aug. 7. With the spacecraft now approximately 5.9 billion miles (9.5 billion kilometers) from Earth, the radio signals carrying that confirmation took about 8 hours and 52 minutes to reach the APL Mission Operations Center via NASA’s Deep Space Network station near Madrid, Spain.

The mission team typically places New Horizons in resource‑saving hibernation mode during long cruise periods. While the spacecraft is hibernating, operators do not send commands or retrieve data, but the spacecraft continues gathering and storing data around the clock from its heliospheric plasma sensors, Solar Wind at Pluto and the Pluto Energetic Particle Spectrometer Science Investigation, as well as its space dust detector, the Venetia Burney Student Dust Counter.

Alice Bowman, the New Horizons mission operations manager at APL, said the spacecraft reported back to Earth, via the Deep Space Network, with a weekly status beacon. “Every status report through this hibernation period was ‘green,’ meaning all was well aboard New Horizons each and every week,” she said.

As New Horizons resumes active operations, Bowman noted, the team will begin downlinking spacecraft health and safety data, followed by data from the three scientific instruments. In about three weeks, the spacecraft’s onboard Alice ultraviolet spectrograph will look at the hydrogen gas distribution in the outer heliosphere, while the Solar Wind at Pluto, the Pluto Energetic Particle Spectrometer Science Investigation, and the Venetia Burney Student Dust Counter instruments continue their measurements, and the ground team conducts a series of spacecraft and instrument checkouts.

The team also is completing upgrades to the ground‑system software that will make it easier to maintain operations of the spacecraft. Tests are already underway and are expected to continue through the year.

New Horizons is operating on updated autonomy logic designed for operations farther from the Sun and to accommodate the expected reduction in power and the naturally occurring increase in radio‑signal travel time.

The NASA spacecraft’s exploration of this distant region of the solar system marks the latest step in a journey that began in January 2006 with the fastest launch on record; a flyby of Jupiter in February 2007 that included stunning views of the gas giant and its moons; the first exploration through the Pluto system in July 2015; the first exploration of a Kuiper Belt object, Arrokoth, in January 2019, and unique studies of the Sun’s outer heliosphere and dozens of additional Kuiper Belt objects since then.

For more information on NASA’s New Horizons mission, visit:

https://science.nasa.gov/mission/new-horizons/

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Discover More Topics From NASA

New Horizons

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Private aviation runs on tight margins and tighter schedules. The AI tools Jet.AI built to optimize both placed the company in an unusual vantage point: watching production inference workloads run against real operational constraints, before the data center power shortage became a mainstream story. Mike Winston, investor and founder of Jet.AI (NASDAQ: JTAI), built those tools inside an operating aviation business and drew from them a conclusion that now anchors two public companies: the constraint binding the AI infrastructure buildout is power, and the gap between available supply and projected demand will persist for years. That conclusion informs the February 2025 agreement to transfer Jet.AI’s aviation operations to flyExclusive, the data center development pipeline being assembled through the Convergence Compute joint venture, and the $138 million SPAC raised through AI Infrastructure Acquisition Corp. (NYSE: AIIA). For investors trying to understand Jet.AI’s trajectory, the aviation chapter is where the thesis actually originates.

From Jet Token to Jet.AI: A Sequence With a Logic

What happened at Jet.AI between 2016 and 2025 reads, from the outside, as a series of technology pivots. The company began as Jet Token, a blockchain-based private aviation startup founded by Mike Winston, CFA, whose prior career had run from equity research at Credit Suisse First Boston through five years as a portfolio manager in merger arbitrage and event-driven investing at Millennium Partners. Regulatory constraints closed off the blockchain model’s commercial path. The company rebuilt around AI tools for aviation: agentic booking software, route optimization for fuel and carbon efficiency, dynamic pricing for charter operations. Each change tracked external conditions. Each stage also produced information the next depended on.

What Building Aviation AI Software Actually Reveals

The tools Jet.AI developed for private aviation required real compute at operational scale. Agentic booking software coordinates availability, pricing, and scheduling across multiple aircraft against a customer base with variable and often short-notice demand. Route optimization requires running real-time models against weather, airspace, and fuel data. Dynamic pricing models consume compute at a rate that scales with transaction volume and prediction complexity.

Running those workloads inside an operating aviation company (not in a research environment, in production, against real cost constraints) produces a specific kind of knowledge. The compute requirements of operational AI are higher than they appear from the outside. The power requirements of compute at scale are higher still.

“Through building AI tools for aviation, we saw firsthand the scale of transformation AI would bring,” Winston said in an April 2026 interview. “That led us to data centers, where the infrastructure opportunity is significant. Given my background in real estate finance and telecom, it was a natural transition. Today, we’re extending that into power generation using aero-derivative engines, another area with strong underlying demand.”

That insight came from operating a business where AI was a production tool, measured against real cost constraints.

The Power Problem, Quantified

The constraint Winston identified by operating inside aviation AI is now visible across the broader market.

The U.S. Department of Energy estimated data center electricity consumption at 176 terawatt-hours in 2023. Analysis by Alderman & Co. projects that figure could reach 580 TWh by 2028. That would put data centers at between 6.7% and 12% of all U.S. electricity. Grid interconnection queues in some U.S. jurisdictions now run eight to 10 years, measured from the point of application.

New gas turbines from major manufacturers are not closing that gap fast enough. Contact GE Vernova today for an LM6000 order and the delivery window runs three to five years minimum. GE Vernova CEO Scott Strazik said in early 2025 that the company expected to be largely sold out through the end of 2028 by that summer. Siemens Energy reported that more than 60% of its U.S. gas turbine orders that year were linked to AI data center demand. Mitsubishi’s newer turbine blocks ordered in 2025 may not ship until the 2030s.

The practical solution for data center operators who need power now is the aero-derivative gas turbine: units built around retired commercial jet engine cores, modified for stationary generation. ProEnergy has sold 21 of its PE6000 units to just two data center projects: more than one gigawatt of combined bridging power. Each unit produces 48 megawatts and can be operational within 30 days of delivery. ProEnergy was quoting 2027 availability when GE Vernova’s order book had already closed into 2028 and beyond.

The Aviation Industry as an Early Observer

The CF6-80C2 turbofan engine, the core unit that ProEnergy overhauls for its ground-based power systems, was widely used on Boeing 767s and Airbus A310s. Approximately 1,000 of these engines are expected to retire from commercial aviation service over the next decade. The supply is quantifiable, the retirement schedule is predictable, and the companies with operational knowledge of aviation hardware were positioned to recognize the secondary market forming around those cores.

Jet.AI was an aviation company with AI tools and capital markets literacy. That combination produced an earlier read on the intersection of retiring aviation hardware and data center power demand than financial analysis alone typically generates.

The competition for aero-derivative turbines has since created cross-sector friction that Alderman & Co. analysts Ryan Kirby and Joseph Lakaj documented in March 2026: aero-derivative units share a near-identical manufacturing base with commercial flight engines, relying on the same specialized castings, high-temperature alloys, and precision forgings. A large data center order for turbines now directly competes with engine deliveries for new commercial aircraft. Boeing and Airbus are both navigating extended delivery timelines driven in part by engine shortfalls. Two industries are pulling on the same supply chain, and the aviation sector is both a contributor to that constraint and, through companies like Jet.AI, a beneficiary of it.

The flyExclusive Transaction and What It Unlocked

The agreement to transfer Jet.AI’s aviation operations to flyExclusive removed the operational complexity that had kept two structurally different businesses inside a single public vehicle.

flyExclusive takes the Citation and HondaJet fleet and the private aviation customer base. The combined platform has the scale to extract returns Jet.AI’s aviation division could not reach independently. Jet.AI shareholders receive flyExclusive (NYSE American: FLYX) equity alongside their retained JTAI position. The post-close version of Jet.AI carries no fleet, no pilots, and no charter operating costs.

What remains in JTAI: the Convergence Compute joint venture with Consensus Core Technologies, targeting one gigawatt of data center capacity across three campuses in North America; a $5 million economic interest in a special purpose vehicle anchored by SpaceX and xAI equity; and the 49.5% economic stake in the AIIA sponsor.

On June 1, 2026, Glass Lewis issued a “FOR” recommendation on the flyExclusive merger. Glass Lewis is one of two proxy advisory firms whose research institutional investors consult as a standard checkpoint before shareholder votes. The special shareholder meeting is scheduled for June 11, 2026. Approval requires an affirmative vote from a majority of all outstanding shares. Institutional participation is essential to clearing that threshold.

Public markets tend to undervalue companies that operate across two structurally distinct businesses. Aviation and AI infrastructure attract different investors on different time horizons. Separating them into distinct listed vehicles removes the valuation friction that a mixed balance sheet creates.

AI Infrastructure Acquisition Corp.

AIIA raised $138 million in its October 2025 IPO. Its mandate is to identify and close a business combination in data center infrastructure or AI, a focus the company describes as “ship to grid.” As of early 2026, management confirmed active engagement with several targets.

The connection to JTAI runs through sponsor economics. SPAC sponsors typically receive 20% of post-IPO equity as founder shares plus warrants exercisable at $11.50. Jet.AI’s 49.5% position in the AIIA sponsor entity means that if AIIA closes a qualifying business combination, nearly half the sponsor economics flow back to JTAI shareholders. The stake was carried at $17.23 million on Jet.AI’s balance sheet as of Q1 2026, and the company reported $13.5 million in cash with no debt.

Winston has positioned the infrastructure bet across two independent paths: an organic buildout through Convergence Compute and an acquisition vehicle through AIIA. The structure means not every outcome depends on a single execution.

Winston’s Background and the Pattern It Reveals

Winston joined Credit Suisse First Boston in 1999 on a telecom research team that Institutional Investor Magazine ranked first, at the start of one of the largest infrastructure capital cycles of the modern era. Five years at Millennium Partners followed, co-managing a $1 billion merger arbitrage and event-driven book through Catapult Capital Management. That discipline produces a specific habit: determine what an asset is worth if the market-moving event does not occur, then price accordingly.

The data center power thesis runs through that same lens. The demand is documented: grid interconnection timelines, turbine manufacturing lead times, and hyperscaler capex commitments are all public record. The question event-driven analysis poses is not whether the demand is real but whether the specific positioning captures the value before it prices in. Winston has spent a career in disciplines that reward being right about that second question.

He founded Sutton View Capital in 2012 after departing Millennium Partners. The firm advised one of the largest academic endowments in the world and co-led successful activist litigation against the Dole Foods board, securing a 35% increase in total consideration for shareholders. The CFA credential, the Institutional Investor ranking, the Columbia MBA: the credentials are institutional. The career decisions have been independent. Jet.AI and AIIA are both built outside established platforms, on conviction about where specific structural conditions point.

Where the Risk Lives

AIIA has a standard SPAC window of 18 to 24 months from its October 2025 IPO. No business combination has been announced. The clock is running, and trust account mechanics create real deadline pressure regardless of whether the acquisition market cooperates on the same schedule.

Convergence Compute has three of four development milestones complete, with power studies and permitting underway across its three campus sites. Construction, equipment procurement, and customer acquisition follow. The financial returns depend on those campuses being built, leased, and stabilized. Each step carries execution risk appropriate to a company of JTAI’s current scale.

The supply constraints that make the thesis credible are also the supply constraints that make execution difficult. Developer competition for turbine delivery slots, permitting capacity, and project financing is intensifying as more capital chases the same infrastructure gap.

The observational logic that runs from aviation AI tools to data center infrastructure holds up as an account of how Winston read the market. Whether Jet.AI can execute against it before the supply advantage narrows is what the next 18 months will determine.

Disclosure: This article discusses Jet.AI, Inc. (NASDAQ: JTAI) and AI Infrastructure Acquisition Corp. (NYSE: AIIA). Readers should conduct their own due diligence before making investment decisions. This piece reflects publicly available information and does not constitute investment advice.

The post Mike Winston on Why Jet.AI Shifted From Aviation to AI Infrastructure appeared first on IT Security Guru.

Ask George Murnane how to separate real artificial intelligence from a marketing slogan, and he gives you one question: what does the model predict, and what is its loss function?

George Peter Murnane has spent more than three decades running asset-intensive aviation businesses, 14 of those years as a chief operating officer, a chief financial officer, or both at once. He is now chief executive of Jet.AI Inc. (NASDAQ: JTAI) and a director and CFO of AI Infrastructure Acquisition Corp., the blank-check company that closed an upsized $138 million IPO in October 2025. That résumé sits at the exact junction where capital, operations, and AI claims collide. It also makes him unusually hard to sell to.

The George Murnane filter: name the prediction, name the loss function

The test is deliberately unglamorous. If a company can tell you precisely what its model forecasts and what error it is trained to minimize, the AI is probably real. If the best it can offer is that the technology “makes the experience smarter,” the label is doing work the software cannot.

That distinction matters more in aviation than in almost any other industry, because the cost base is high and the margins are thin enough that a small efficiency gain compounds into real money. Murnane’s filter is a way of routing scarce capital toward the few applications that move those numbers, and away from the many that only move a pitch deck.

Where AI is real in aviation: the AOG math

Start with predictive maintenance, the application Murnane considers genuinely valuable. The economics are not subtle. A single aircraft-on-ground event can cost an operator between $10,000 and $150,000 per hour of downtime, once you add lost revenue, crew rest and overtime, passenger re-accommodation, and the scramble to source a replacement part.

Predictive maintenance attacks that cost directly. By reading sensor data, flight history, and maintenance records, the models flag a deteriorating component before it fails, which lets an operator move an unplanned repair into a scheduled window. A 2022 Deloitte analysis cited by Radome Technologies estimated that predictive maintenance, properly implemented, can cut maintenance costs by up to 30% and reduce AOG events by more than half. Delta cut unscheduled maintenance by more than 30% using predictive engine monitoring.

The use case is specific enough to survive Murnane’s question. The model predicts a component failure. Its loss function penalizes false negatives, the missed failures that ground an aircraft, and false positives, the needless part swaps that waste a maintenance slot. There is a number on both sides of the ledger. Investors have noticed the same thing: the predictive airplane maintenance market is projected to grow from $5.35 billion in 2026 to $18.87 billion by 2034, a compound annual rate above 17%.

Predictive maintenance is not the only application that clears the bar. Crew scheduling optimized against duty-time limits has a defined objective and a hard constraint set written into federal regulation. Dynamic pricing run against forward booking curves predicts demand and optimizes yield. Document automation in SEC filings and merger diligence has a measurable output and a measurable error rate. Each of these can be described in a sentence that names what is being predicted. That is the tell.

The failure modes George Murnane watches for

The opposite of a loss function is an adjective. Murnane’s interviews return repeatedly to two ways companies dress up old or absent technology as AI.

The first is rebranding. A regression model that has been forecasting demand or pricing risk for 30 years gets relabeled “AI” because the term raises a valuation. The math is the same forecast it always was, repackaged under a more valuable label.

The second failure mode is more current and more expensive. A company bolts a large language model onto a workflow without redesigning the workflow underneath it. The result is a chatbot marginally more eloquent than the FAQ page it replaced, sold as a transformation. The model is real, but the value is not, because no one re-engineered the process the model was supposed to improve.

Murnane’s caution here is partly reputational arithmetic. As he has put it, the credibility cost of overclaiming compounds faster than the marketing benefit. For a public company whose name carries the letters “AI,” that is not an abstract risk. Overstate what the software does, and the first product failure under pressure becomes the story.

How Jet.AI uses AI where the value is measurable

Jet.AI gives Murnane a place to apply his own test in public. The company’s software, built when it operated as a private-aviation platform, concentrated AI on functions with a number attached: booking optimization, matching customers to the right operator, and customer communication. Its CharterGPT app and the Ava agentic booking model used natural-language processing to compress a booking process that once ran on phone calls and faxes.

Those tools handle a deliberately narrow set of jobs. Flying the airplane, vetting an operator’s safety record, and resolving a mechanical failure at midnight stay with humans and regulators. The AI sits where its prediction is cheap to measure and its errors are cheap to correct, which is exactly where Murnane argues it belongs.

From booking software to AI data center infrastructure

The most telling application of the loss-function test is the one Jet.AI is now living through. The company has moved away from running aircraft and toward building AI data center infrastructure, describing itself as a technology company focused on data center development across North America, with projects spanning more than a gigawatt of planned capacity.

The reason behind the pivot reads like a case study in Murnane’s own discipline. Jet.AI built genuine AI products, including a large language model agent for private aviation, then ran into a constraint that no amount of marketing could fix: unreliable uptime for the computational resources those products depended on, which occasionally slowed the company’s ability to serve customers. The bottleneck sat below the algorithm, in the power, land, and compute the products ran on.

So the company went after the bottleneck. Based in Las Vegas, with access to land, solar power, and natural gas, Jet.AI signed a letter of intent for a 50-megawatt project on a 120-acre campus with room to scale toward a full gigawatt. The framing Executive Chairman Mike Winston used was almost a rebuke of the category’s usual rhetoric: the move “isn’t a flashy move, but it’s a smart one,” because data centers are “the bedrock of the AI economy” and create value that is “tangible, stable, and meaningful.”

That is the loss-function test pointed at infrastructure rather than software. The prediction is straightforward: compute demand keeps climbing, and the assets that supply it earn against it. The error is measurable in megawatts delivered and uptime maintained. There is a number on both sides.

Why the loss-function test travels

Murnane’s filter works because it is industry-agnostic. It ignores whether a technology looks impressive and asks instead whether anyone can state what the system is optimizing and check the result against reality.

That discipline is the through-line of his career, from pricing aircraft assets at global carriers to evaluating a data center SPAC. The same question that exposes a relabeled regression model also exposes an overhyped acquisition target. In both cases, the executive who can describe the objective function is operating from evidence. The one reaching for “smarter” and “seamless” is operating from hope.

For a sector where roughly every company now claims an AI strategy, the value of a one-question screen is that it is fast and hard to fake. Name the prediction. Name the loss function. If those two answers are specific, the technology is likely doing real work. If they dissolve into adjectives, the only thing being optimized is the marketing.

The post George Murnane’s One-Question Test for Real AI appeared first on IT Security Guru.

Huntress has struck a new distribution partnership with UK channel marketplace Giacom, giving the managed service providers (MSPs) on Giacom’s Cloud Market platform direct access to Huntress’ Agentic Security Platform and its 24/7 AI-centric Security Operations Centre (SOC). 

The deal is one of two announced this week, alongside a parallel agreement with MSP Nordics covering Denmark, Finland, Iceland, Norway and Sweden, as Huntress looks to broaden its footprint across EMEA. Both moves are aimed at removing friction for MSPs that want to add enterprise-grade detection and response capability without taking on new vendor relationships from scratch. 

Giacom supports more than 6,000 MSPs and technology providers in the UK through its Cloud Market platform, which bundles cloud, connectivity, mobile, hardware and security offerings from multiple vendors alongside sales, management and enablement tooling. Bringing Huntress into that catalogue means Giacom partners can now sell managed EDR, identity threat detection and response (ITDR), identity and endpoint security posture management, managed SIEM and security awareness training through a distributor relationship many of them already use.  

For Huntress, the tie-up also reinforces its existing alliance with Microsoft, giving the vendor a path to partners through Giacom’s established Microsoft practice, a route the company says will help it reach MSPs that remain heavily exposed to ransomware, business email compromise, account takeover, phishing and abuse of legitimate remote management tools. 

“MSPs are under increasing pressure to deliver stronger security outcomes for customers without adding complexity to their own operations,” said Carl Oliver, Head of Product & Cloud Practice at Giacom. “Huntress stands out for its ability to deliver high-quality managed detection and response in a way that is purpose-built for MSPs, backed by around-the-clock specialist support. By bringing Huntress into our portfolio, we are giving our partners another way to strengthen their security services, support more customers with confidence, and create new opportunities for growth.”

“Scaling security across regions depends on trusted relationships within those markets,” added Kevin Hallmark, Head of Global Distribution at Huntress. “Giacom and MSP Nordics bring the relationships, regional expertise, and partner-first approach needed to help us equip more MSPs to defend businesses across the UK and Nordics that remain most exposed to today’s cybercriminals.”

Huntress says its platform currently safeguards more than five million endpoints and 13 million identities worldwide, with its analyst-led SOC often among the first responders to major incidents affecting the security community. The company positions itself as making enterprise-grade protection accessible to businesses that would otherwise lack the budget or in-house expertise to defend against modern threats. 

 The Giacom and MSP Nordics agreements follow a pattern familiar to the channel: rather than selling direct, security vendors increasingly lean on regional distributors with existing trust and reach to accelerate adoption among smaller and mid-sized MSPs.  

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Throughout human history, technology has changed what we think is possible. Once, communication meant sending a letter. Now, we are all buried under a constant stream of emails, messages and notifications.

AI marks another major inflection point. For the first time, ordinary people can communicate with computers in natural language, not code. That shift is already reshaping whole industries, changing how work gets done, how decisions are made, and how quickly ideas can move from concept to reality.

In this article, I’ll look at three industries already being transformed by AI, and one that could be next.

DNA research

DNA research may not be the first industry that comes to mind when people think about AI, but it has already been transformed by it.

The reason is simple: AI is extremely good at processing vast amounts of complex data. In genetics, that matters enormously. Tools such as Google DeepMind’s AlphaGenome developed by the team led by Demis Hassabis, are helping researchers analyse DNA with a level of speed and precision that would have been unthinkable only a few years ago.

Calculations that once took hours can now be completed in seconds. That gives scientists a clearer view of how DNA works, how mutations develop, and what those mutations might mean for human health.

In practical terms, AI can help researchers track changes in DNA and forecast their likely impact. That gives scientists a serious head start in identifying which mutations matter, why they matter, and how they might contribute to disease. It is exactly the kind of groundwork that future cancer treatments will be built on.

Intelligent communication

AI is also changing the way businesses understand their own conversations.

Until recently, meetings, sales calls and internal discussions were easy to lose. Someone might take notes, but those notes were often incomplete, inconsistent or forgotten. Now, AI can record, transcribe, analyse and summarise those conversations automatically. Instead of rushing to capture every detail, teams can focus on the discussion itself.

Tools like XFactorAi, founded by AI entrepreneur John Margerison, take this a step further. Rather than simply telling you what was said, they can help identify what matters. They can flag risks, highlight opportunities, spot urgent follow-ups and show where action is needed.

That is a meaningful shift. It moves business communication from “here is a transcript” to “here is what you should do next.”

The real value is not just the time saved, although that is significant. It is the intelligence created from conversations that would otherwise disappear. Companies that properly analyse their meetings, sales calls and customer interactions can spot patterns faster. They can see what customers are asking for, where frustrations are building, and where opportunities are being missed.

Listening better has always been good business. AI simply makes it easier to listen at scale.

Advertising and branding

Image generation is still a relatively new part of AI, but it is already changing advertising, branding and creative production.

For small businesses, this is a huge breakthrough. Companies like Sourceful are making it possible to turn a website, logo or product image into professional-grade visual assets. What once required a studio, photographer, production team and significant budget can now be produced much more quickly and affordably.

That changes what is possible for smaller brands. High-quality creative is no longer limited to companies with large marketing budgets.

AI is also changing the production process for larger design and e-commerce teams. Tools like RiverflowAI can help reduce the cost and complexity of producing advertising assets. If a product image does not look quite right after the shoot has finished, the answer no longer has to be another expensive day in the studio. AI can adjust the image, refine the setting, change the background, and help creative teams get closer to the result they need.

If everything was shot against a pink background but the creative director now wants blue, that kind of change can be made quickly. What used to be a logistical problem becomes a creative adjustment.

That is why AI is so significant for advertising. It does not just make production cheaper. It makes creative work more flexible.

Forward-looking farming

Farming could be one of the next industries to be elevated by AI.

Take pesticide use. Traditionally, a farmer might spray a whole field to deal with weeds. With AI-powered computer vision, tractors can identify exactly which areas need treatment and apply pesticide only where it is required. Companies such as John Deere, led by CEO John May, are already developing this kind of precision agriculture technology, using AI and automation to help farmers treat crops more accurately. That reduces waste, protects healthy crops and lowers the amount of harsh chemicals used across the field.

The same logic applies to autonomous machinery. As self-driving vehicles become more advanced, the role of the farmer will start to change. Instead of operating one tractor at a time, farmers may increasingly manage fleets of autonomous machines working across thousands of acres.

That could make farms far more productive. A coordinated fleet could harvest faster, work longer hours, and operate during critical seasonal windows when timing matters most.

The challenge is infrastructure. Many rural areas still lack the reliable connectivity needed to support cloud-based AI systems. If a machine needs to send huge amounts of real-time data to a distant server and wait for instructions, a weak connection can create serious problems.

That creates a frustrating paradox. In many cases, the technology is ready, but the infrastructure is not.

Still, the direction of travel is clear. AI has the potential to make farming more precise, less wasteful and more productive.

From decoding cancer mutations to spotting weeds in a wheat field, the pattern is the same: AI is not simply replacing people. It is expanding what people are able to do.

The most exciting use of AI is not automation for its own sake. It is capability. It gives scientists, sales teams, designers and farmers new ways to understand problems, make decisions and act faster.

We are still at an early stage, but the direction is obvious. The industries that learn how to use AI well will move faster, operate smarter and create more value. Those that treat it as a passing trend risk being left behind.

The post The industries being reimagined by AI appeared first on IT Security Guru.

If you’ve recently started learning ethical hacking, you’ve probably encountered countless videos, blog posts and social media content claiming that hacking is easy, exciting and something you can master in a few weeks. Unfortunately, much of this information is misleading. Movies, television shows and clickbait content have created unrealistic expectations about what ethical hacking actually […]

The post Ethical Hacking Myths: Common Misconceptions Beginners Believe appeared first on Hackercool Magazine.

Starting a cybersecurity journey is exciting. You discover ethical hacking, penetration testing, digital forensics, threat hunting, malware analysis and dozens of other fascinating areas. Suddenly, a whole new world of technology opens up. Unfortunately, many beginners make mistakes that slow their progress, create unnecessary frustration or even push them away from cybersecurity altogether. The good […]

The post 5 Things Beginners Should NOT Do When Starting Cybersecurity appeared first on Hackercool Magazine.

As July begins, Shiba Inu investors are closely monitoring the market for signs of recovery after the token endured a brutal June. The month of June marked SHIB's most bearish period of 2026 and left market participants uncertain about the weeks ahead.

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