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Forrester on cybersecurity budgeting: 2025 will be the year of CISO fiscal accountability

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More With 90% of cybersecurity and risk leaders predicting they’ll see budget increases in 2025, many are facing a new era of accountability, with boards wanting to see solid returns on cybersecurity investments. That’s an elusive expectation […]

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With 90% of cybersecurity and risk leaders predicting they’ll see budget increases in 2025, many are facing a new era of accountability, with boards wanting to see solid returns on cybersecurity investments.

That’s an elusive expectation to deliver on, given that 35.9% of a typical CISO’s budget is going for software. Knowing if, how, when and under what conditions a given cybersecurity software investment delivers a hard-number-based ROI is not easy to do, and such numbers of hard to prove.

Clear budget wins do exist, though. They start with automating security operations center (SOC) workflows that are overwhelming analysts with too many conflicting alerts. Automating an endpoint detection and response system is one good place to start, with the goal of reducing alert fatigue in SOCs so analysts can focus on more complex threats and intrusion attempts. Another is automating patch management. CISOs need to move beyond trying to get this done manually with overextended teams, and automate it using the latest AI- and ML-based platforms purpose-built for optimizing patch management network-wide.

Forrester’sBudget Planning Guide 2025: Security and Risk” provides insights into why CISOs are seeing their budgets preserved when other areas of an organization are experiencing layoffs, budget cuts, and, in some cases, new programs being put on hold or canceled altogether. (Note, however, that cybersecurity budgets are, on average, just 5.7% of IT annual spending.)

Gartner’s latest forecast update (4Q 2024) of end-user spending for information security reflects the resilience of CISOs’ budgets in the aggregate. These budgets are predicted to grow from $184 billion in 2024 to $294 billion in 2028, and Gartner forecasts the market will grow at a 12.43% compound annual growth rate (CAGR) in four years. Security software is expected to be the fastest-growing segment, consistent with Forrester’s recent findings of CISO spending benchmarks. Gartner predicts spending on security software will grow from $59.9 billion in 2022 to $134.3 billion in 2028, attaining a CAGR of 14.4%.

The 10 fastest-growing market segments are outperforming the aggregate market by a slim margin of 12.63%, with cloud security the fastest-growing segment, projected to attain a CAGR of 25.87% from 2024 to 2028.  

2025 is shaping up to be the year of CISO fiscal accountability

Stephanie Balaouras, Forrester vice president, group director, stated in a recent webinar, “When you think about AI, when you think about some of the novel threats that we’re looking at, when you think about post-quantum encryption, [and] the concerns about that, we are at this inflection point.” Gartner predicts that by 2028, 22% of cyberattacks and data leaks will involve generative AI.

Boards aren’t stopping there. While they’re funding the realities of this inflection point by approving security budgets and, in some cases, increasing them, they’re most focused on cutting tech stack sprawl and the expensive licensing fees needed to keep the tech running. Boards’ approval of budgets to improve compliance, reduce AI risks, and reduce tech stack sprawl all hinge on CISOs and their teams delivering this year.

Reading between the lines of Forrester’s budget report, we can see that CISOs have entered a new era of accountability.

How CISOs are optimizing cybersecurity spending to make the most impact

Cloud infrastructure, data, and software are where CISOs are prioritizing their budgets going into 2025, with data-related investments anticipated to make the most significant impact.

Forrester sees the increasing adoption of AI and generative AI (gen AI) as driving the needed updates to infrastructure. “Any Gen AI project that we discussed with customers ultimately becomes a data integration project,” says Pascal Matska, vice president and research director at Forrester.

“You have to invest into specific capabilities and platforms that run specific AI workloads in the most suitable infrastructure at the right price point, and also drive investments into cloud-native technologies such as Kubernetes and containers and modern data platforms that really are there to help you drive out some of the frictions that exist within the different business silos,” Matska continued.

Security and risk leaders are anticipating the most significant changes in their budget next year to be in cloud security, investing in new security technology to run on-premises, and security awareness and training initiatives. Each of those areas is projected to see an increase of 10% or more in 2025 budgets.

Protecting revenue is core to CISO accountability

One of the most valuable takeaways from Forrester’s cybersecurity planning guide is how essential it is for CISOs to take responsibility for protecting revenue if they want to stand a chance of implementing the guide’s recommendations. VentureBeat continues to see that successful CISOs know how to lead their teams to support and protect revenue, and are often included in board-level discussions and report to the CEO.

CISOs who drive gains in revenue advance their careers. “When something touches as much revenue as cybersecurity does, it is a core competency. And you can’t argue that it isn’t,” Jeff Pollard, VP and principal analyst at Forrester, said during his keynote titled “Cybersecurity Drives Revenue: How to Win Every Budget Battle” at the company’s Security and Risk Forum in 2022.

Budgeting to protect revenue needs to start with the weakest, most at-risk areas. These include software supply chain security, API security, human risk management, and IoT/OT threat detection. Software supply chains are under siege, with 91% of enterprises falling victim to security incidents in just a year, underscoring the need for better safeguards for continuous integration/continuous deployment (CI/CD) pipelines.

Open-source libraries, third-party development tools, and legacy APIs created years ago are just a few threat vectors that make software supply chains and APIs more vulnerable. Persistent attacks on open-source components with wide distribution, including the Log4j vulnerability, are fueling more significant investment in software supply chain security.

Where CISOs plan to invest in new technologies

Forrester advises CISOs to consider investing in four new technology areas, briefly described below:  

Exposure management and cyber risk quantification: As enterprises begin creating more of their AI-based apps internally and expand into devops, cloud, and IoT, vulnerability risk management (VRM) and attack surface management (ASM) become mission-critical. CrowdStrike often calls this Falcon exposure management, while Trend Micro and others refer to it as attack surface management. Coupled with cyber risk quantification (CRQ) capabilities, these solutions help security leaders see which fixes produce the most significant risk reduction. CEO and founder George Kurtz of CrowdStrike told VentureBeat in an interview, “One of the areas that we’ve really pioneered is that we can take weak signals from across different endpoints. And we can link these together to find novel detections. We’re now extending that to our third-party partners so that we can look at other weak signals across not only endpoints but across domains and come up with a novel detection.”

Post-quantum security and crypto agility: “Q-Day,” when quantum computers can break today’s RSA and elliptic-curve cryptography, is still years away by many estimates. But that’s not stopping enterprises from investing in new technologies to meet this threat today. Forrester advises prioritizing data discovery and acquisition audits, especially for financial services companies and government agencies.

Security data lakes: High-profile acquisitions and mergers in this area, including Cisco’s purchase of Splunk, LogRhythm merging with Exabeam, and IBM selling QRadar SaaS to Palo Alto Networks, alerts us that this an area every CISO needs to pay attention to, given the ongoing innovations and the possible price savings. VentureBeat is finding that enterprises are increasingly evaluating security data lakes, like Amazon Security Lake, Snowflake, and Google BigQuery, as solutions for storing security data without the high cost of traditional SIEM platforms. Forrester cautions SIEM platforms to defy quick, economical integration, however. Look for security providers that offer ready-made integrations with leading data lakes. Cisco, CrowdStrike, Ivanti, Zscaler and others provide hooks for ingesting, analyzing or automating data workflows in third-party lakes.

AI and ML security: “It’s tough to go out and do something if AI is thought about as a bolt-on; you have to think about it [separately],” Jeetu Patel, EVP and GM of security and collaboration for Cisco, told VentureBeat, citing findings from the 2024 Cisco Cybersecurity Readiness Index. “The operative word over here is AI being used natively in your core infrastructure.” That’s solid advice for any CISO defending a budget that includes AI and ML apps and components. VentureBeat continues to see platforms designed with AI at their core being the most effective against multidomain breach attempts. Adam Meyers, SVP of intelligence at CrowdStrike, told VentureBeat during a recent press briefing that “it’s also important to note that lots of organizations are implementing their own AI, and so what we’re actually looking at from a next-generation threat perspective is AI workloads, because every organization in the world, I would imagine in the next couple of years, is going to be running their AI. We need to protect those AI workloads as well.”

CISOs need to think ahead about how best to protect data, infrastructure, support apps and the workloads required to get security rights for the enterprise-wide deployment of AI and gen AI.

CIOs and CISOs need to join forces in 2025 to deliver ROI

CISO-CIO alignment will be critical in 2025. This collaboration is essential to excel at securing businesses. Bob Grazioli, CIO, Ivanti advised CISOs during a recent interview with VentureBeat that “executives need to consolidate resources — budgets, personnel, data and technology — to enhance an organization’s security posture. A key priority for CIOs next year will be ensuring that C-suite members leverage AI-driven insights to inform business outcomes, not just technical outcomes.”

Grazioli continued, “However, investments in AI are undermined by a lack of data accessibility and visibility. To address this, data silos between departments such as [those overseen by] the CIO and CISO must be eliminated. AI has the potential to become a centralized source of information, significantly reducing workloads for IT personnel and providing security with a holistic view of an organization’s risk landscape. Achieving that level of visibility increases the probability CISOs will be able to deliver the results they’re trying to achieve.”

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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