Month: August 2024

Only 17% of the organizations we surveyed have a formal AI policy in place despite employees using AI at work.

Artificial intelligence is already changing professional working patterns across almost every industry. It has the power to drastically reduce the time we spend on routine tasks and free us up to think more strategically in our day-to-day professional lives.

This is no different for the IT and cybersecurity sector – at ISACA, our survey of business and IT professionals in Europe found that almost three quarters (73%) of the businesses we surveyed reported that their staff use AI at work.

Yet the key issue with AI, as transformative as it can be, is that we need to ensure we are using it responsibly and securely. After all, LLMs are trained on data which is oftentimes sensitive, and we need proper guardrails on these programs so that hallucinations do not affect the integrity of our work. Only 17% of the organizations we surveyed have a formal, comprehensive AI policy in place which outlines the business’ approach to these issues and provides best practices for use, despite the fact that employees are using AI at work.

AI is changing the threat landscape

At the same time, cyber criminals also have access to AI, and they’re using it to strengthen their criminal enterprises and capabilities, making their threats more convincing and effective than ever before. Not only does this pose a threat to the individual, but it poses a significant threat to businesses as well. Businesses are interconnected organizations with networks of suppliers and professional relationships – when one suffers a breach, all organizations across the network are at risk.

The recent CrowdStrike IT outage highlights just how vulnerable businesses are should they experience even a single IT fault or cyber attack. When one service provider in the digital supply chain is affected, the whole chain can break, causing large-scale outages – a digital pandemic. One rogue update, the unfortunate result of a lack of foresight and expertise, sparked chaos across a number of critical industries, from aviation and healthcare to banking and broadcasting.

Sometimes such incidents are caused by unintentional mistakes when updating software, and sometimes it is the result of a cyberattack. But the irony is that cybersecurity companies are also part of the supply chain, and those same companies that are fighting to establish cyber resilience may too become victims themselves, affecting service continuity.

Cyber professionals are acutely aware of this fact – when we asked our survey respondents about generative AI’s potential to be exploited by bad actors, 61% of respondents were extremely or very worried that this might happen. When comparing this to our data from last year’s survey, the sentiment has virtually not improved.

Training and upskilling are the key to long-term resilience

AI is being used twofold – bad actors are weaponizing the technology to develop more sophisticated attacks, and in response, it is being used by cyber professionals to keep pace with the evolving threat landscape and better detect and respond to those threats. Employees know that they need to keep pace with cyber criminals, upskill themselves, and really get to grips with AI, but when we asked our survey respondents how familiar they are with AI, almost three quarters (74%) were only somewhat familiar or not very familiar at all.

The CrowdStrike incident has brought the need for a more robust and resilient digital infrastructure to the fore, and the rise of AI will only make cyber threats more significant. It’s important that as an industry, we invest in upskilling and training to avoid similar crises in the future, and advancements in technologies like AI could be the key to working more efficiently. The right protocols must be established well ahead of time to move quickly when attacks and outages happen to minimize the damage and disruption. But this isn’t possible without the people with the skills to establish bespoke security frameworks and ensure everyone involved is trained on how to follow them.

If businesses are to both protect themselves and their partners in the long-term as well as see the benefits of using AI, they need to have the right skills in place in order to be able to identify new threat models, risks and controls. Training in AI across the cybersecurity sector is sorely needed – at the moment, 40% of businesses provide no training to employees in tech positions. Further, 34% of respondents believe that they will need to increase their knowledge of AI in the next 6 months, and in total, an overwhelming 86% of respondents feel that this training will be necessary within the next two years.

By taking an approach to AI which prioritizes training and comprehensive workplace policies, businesses and employees alike can rest assured that they are harnessing AI’s potential and keeping pace with cyber threats as they evolve in a secure and responsible manner, protecting both the business itself and every other enterprise within their wider network.

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This article was produced as part of TechRadarPro’s Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

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This rise in energy consumption isn’t just a tech issue; it’s a big environmental concern that everyone in the AI field needs to tackle.

The rapid growth of AI has changed many industries and led to amazing new technology, but it also comes with a big problem: a huge increase in energy use. This rise in energy consumption isn’t just a tech issue; it’s a big environmental concern that everyone in the AI field needs to tackle. As AI continues to develop, it’s not just about making smarter models and solving more users’ problems; it’s about making sure these advancements don’t harm our planet. The question isn’t just about what AI can do for us, but how we can ensure its advancements are sustainable for the planet.

The current landscape

Gartner predicts that without sustainable AI practices, by 2025, AI will consume more energy than the human workforce, significantly offsetting carbon-zero gains.

According to a recent report from the Federal Energy Regulatory Commission, data center demand in the US is expected to reach 35 gigawatts by 2030, that’s the equivalent of powering about 26 million homes. (For context, 1 GW is enough energy to power about 750,000 homes.

In regions like Salt Lake City where gigantic energy users including Meta and Google are building data centers, there has been a noticeable shift back to coal as more data centers are needed to support AI workloads. Plans to retire coal-fired power plants early are being abandoned, pushing the dates as far back as 2042 and dialing back on clean energy resources.

This is a concerning shift that underscores the complex trade-offs between technological advancement and sustainability, especially as AI is on its way to driving a 160% increase in data center power demand by 2030.

While some tech giants like Google, Amazon, and Microsoft have committed to powering their data centers with 100% renewable energy by 2030, the current landscape still sees significant carbon footprints from AI operations.

Based on public data from Meta, one of its data centers in Iowa uses the annual equivalent amount of power as 7 million laptops running eight hours every day.

According to a study from Hugging Face and Carnegie Mellon University, creating an image using generative AI takes as much energy as fully charging your smartphone.

ChatGPT queries consume nearly 10 times the amount of electricity as a Google search. For a startup, training its AI models in the US consumes ~1000 tons of CO2 in a year — the equivalent of 1000 Paris to NYC trips.

AI needs an energy breakthrough. The industry is exploring solutions like atomic fusion to speed up the energy transition away from fossil fuels, but until this energy breakthrough happens, people and businesses in AI need to take individual steps toward change.

Why the AI industry has been slow to adopt sustainable practices

AI-led businesses face challenges in the areas of technology, financial investment, and stakeholder engagement when trying to adopt sustainable AI practices.

Transitioning to sustainable AI solutions often requires substantial upfront investment in energy-efficient technologies and renewable energy sources. According to an IBM sustainability study, while the majority of executives (76%) agree that sustainability is central to their business, nearly half (47%) struggle to fund sustainability investments.

Furthermore, only 31% of organizations report integrating sustainability data extensively into their operational improvements, indicating a gap between sustainability goals and actionable steps.

The shift toward green data centers and sustainable hardware requires not only capital but also a strategic overhaul of existing infrastructures. Companies building AI have complex decisions to make about upgrading to more efficient systems while managing ongoing operational costs.

This, plus the rapid pace of technological change, can make it difficult for businesses to keep up. Many AI companies in early development stages may deprioritize sustainability due to the immediate pressures of competition, technological development, and finding product-market fit.

But as the demand for AI grows, it’s becoming essential for businesses to integrate sustainability into their decision-making processes to achieve environmental goals and drive innovation.

How we can all shape the future of AI sustainability

The entire industry plays a part in influencing a more sustainable future for AI. The readiness, adoption, and development of green AI practices depend on the maturity of the market and stakeholder involvement.

Venture capitalists can evaluate the environmental impact of their portfolio, request impact statements from companies, and share sustainability best practices to inspire more businesses to take action.

Enterprise companies and SMB’s using AI can request environmental impact statements from providers to evaluate sustainability efforts and commitment.

Companies developing AI products can be selective about what type of AI model they use. Recent studies show that specialized AI models consume less energy than general purpose AI models. The more frugal it is, the faster it can execute, improving user experience and reducing energy consumption.

Companies developing AI models can partner with green data centers like Genesis Cloud to leverage renewable energy sources and minimize environmental impact. They can ask cloud providers for the Power Usage Effectiveness (PUE) scores of their data centers and even use an open source tool to measure their cloud carbon footprint. Internally, they can develop more frugal specialist AI models to lower carbon emissions. Externally, they can publish their model CO2 emissions like Meta did for Llama 3.1.

Cloud providers like Amazon Web Services (AWS), Google Cloud, Scaleway, and Genesis can help reduce the carbon footprint of AI by developing infrastructures that maximize energy efficiency and by being transparent. This involves sharing their PUE scores including the energy consumed to cool the data center and the CO2 emissions to build the data center, and potentially offering green pricing options. Data centers can also relay the demand for energy efficient chips to hardware providers.

Hardware providers can develop energy efficient chips like NVIDIA that claims its new “superchip” can boost performance for generative AI tasks by 30 times while consuming 25 times less energy with new chip cooling techniques.

Public funding can participate by integrating carbon footprint assessments into decision-making processes.

Regulators can evolve toward holding all players in the AI ecosystem accountable.

As an industry, we need to take a systemic approach of shared responsibility to reduce the environmental impact of AI at all levels of the ecosystem.

Change can happen now

Keeping up with today’s fast paced AI innovation is crucial for businesses to stay competitive, but with the market maturing, sustainability should play a bigger part in the decision making process.

Take the first step by choosing an AI provider that’s already taking action to reduce its energy consumption — ask for their environmental impact statements or if they measure their company’s carbon footprint.

It’s up to all of us to lead the way and advocate for a more sustainable future for AI.

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This article was produced as part of TechRadarPro’s Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

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