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AI is transforming industries at an incredible pace, but with its power comes significant security risks. From adversarial attacks to data breaches, companies must be prepared to protect the AI-powered applications they build. Yet, how do technology managers approach security and risk management in AI? Which practices are becoming standard, and who is leading the charge? Our recent research sheds light on how organisations secure their AI systems according to technology professionals in leadership positions, revealing some notable gaps. This blog post is based on a bigger report  that talks about trust, risk, and security management in AI overall. The blog narrows down the focus, diving deeper into data collected from 569 professionals in management positions within tech companies, namely tech/engineering team leads, CIOs / CTOs / IT managers, and CEO/management. They answered questions about trust, risk, and security management in AI in the 27th edition of our global Developer Nation Survey , which was fielded in Q3 2024. How are organisations protecting their AI-powered applications? AI security risks range from adversarial attacks and data breaches to model manipulation. To mitigate these threats, organisations deploy various protective measures. Companies are mainly investing in AI-specific security tools and technologies (33%) and encryption tailored for AI data (31%) to stay ahead of potential threats. Regular AI security audits (29%), staff training on AI security risks (29%), and data privacy management for AI (28%) are also common practices among organisations. However, not every organisation has made AI security a priority. While 82% of technology professionals report their company uses at least one mitigation strategy, 10% admit they have no AI-specific risk management in place, and another 8% simply don’t know what their company is doing to address security risks. Nearly one in five technology leaders either have no AI risk strategy or don’t know if their organisation has one Who is driving AI security efforts within organisations? Security is no longer just the responsibility of IT teams. CIOs, CTOs, IT managers, and senior executives (including CEOs) report equal adoption rates of AI-specific security practices, 86% and 85%, respectively. This suggests that AI security is recognised as both a technical challenge and a business priority at the leadership level. However, tech and engineering team leads lag behind, with only 72% reporting the implementation of AI security practices within their organisations. This gap indicates a disconnect between leadership’s security policies and awareness at the development level rather than differing priorities. Team leads may have less visibility into company-wide AI security strategies, which could explain the lower reported adoption. Among technology professionals, tech and engineering team leads report lower awareness of AI security practices within their organisations Company size matters: Are smaller firms falling behind in AI security? Company size plays a significant role in how AI security is handled. Managers in large enterprises (i.e., companies of more than 1,000 employees), with their expansive resources and dedicated security teams, report the highest adoption rate of AI security practices, with 90% implementing such measures. Managers in medium-sized businesses (51-1,000 employees) follow closely at 86%, but those in small businesses (up to 50 employees) lag far behind at just 64%. This gap isn’t just about awareness - it’s about priorities and resources. Large enterprises are far more likely than small businesses to conduct AI-specific penetration testing (32% vs. 10%), regular security audits (34% vs. 18%), and threat intelligence and risk assessments (28% vs. 14%). With tighter budgets and fewer specialised security personnel, smaller companies often struggle to allocate resources for AI-specific protections, relying instead on broader cybersecurity measures that may not fully address AI-related risks. However, medium-sized companies take the lead over both small and large companies when it comes to employing certain AI security practices. They lead in the adoption of AI-specific security tools, with 40% using them compared to 33% of large companies and just 20% of small businesses. Similarly, 35% of medium-sized businesses have data privacy management solutions tailored for AI, surpassing large enterprises at 27% and small businesses at 16%. This suggests that medium-sized companies, while not having the vast resources of large corporations, may be more agile in adopting emerging security technologies, striking a balance between strategy and execution. While large enterprises have the budgets and teams to prioritise AI-specific protections, small businesses struggle to keep up, leaving them more vulnerable to AI-related threats How does AI security vary across development types? AI security is far from uniform across industries. Each sector faces unique challenges shaped by the nature of its AI applications, the volume of data it processes, and the potential risks associated with AI-driven automation. While some industries have embraced AI security as a fundamental requirement, others are lagging, either due to a lack of awareness, lower perceived risks, or resource constraints. At the forefront of AI security adoption are managers involved in consumer electronics (96%), augmented reality (95%), and industrial IoT (95%) projects. Managers in these industries prioritise security not just because of regulatory pressures but also due to the inherent risks associated with their AI-driven operations. Consumer electronics and IoT devices, which process vast amounts of real-time personal and behavioural data, place a heavy focus on robust encryption and access control. In fact, 45% of managers in this sector report implementing these protective measures to prevent data breaches and adversarial attacks. Augmented reality (including mixed reality applications) goes even further, with 59% of managers reporting using encryption measures tailored specifically for AI data. This emphasis likely stems from the fact that AR systems often involve real-time spatial data processing, biometrics, and interactive user engagement, making them highly sensitive to security threats. However, not all sectors demonstrate the same level of urgency when it comes to AI security. Backend services fall significantly behind, with only 69% of managers working in backend reporting that their organisation has AI-specific security measures in place. Of the rest, 16% are unsure whether their company has any AI security practices at all, and a notable 15% confirm that their organisation has no such measures in place. This lack of adoption suggests that backend service providers may still be relying on traditional cybersecurity approaches, underestimating the distinct vulnerabilities that AI-powered applications introduce. Industries handling sensitive consumer data, like consumer electronics and IoT, lead in AI security adoption. However, backend services may be underestimating AI-specific risks by relying on traditional cybersecurity measures. How does experience impact AI security awareness? One of the more unexpected findings in AI security management is that less-experienced managers are more likely to implement AI security measures in their companies than their seasoned counterparts. Managers with less than two years of experience in software development report the highest adoption rate within their organisations, at 90%, while those with over a decade of experience drop to 74%.  This decline could indicate that organisations with more experienced managers rely more on traditional cybersecurity approaches rather than AI-specific frameworks. While awareness levels remain consistent across experience groups, companies led by seasoned professionals may be slower to adapt their security strategies to evolving AI threats. As AI risks become more sophisticated, ensuring that security measures keep pace will require continuous evaluation and adaptation at the organisational level. Organisations with more experienced managers may be slower to adopt AI-specific security frameworks, potentially relying more on traditional cybersecurity approaches Want to dig deeper? This post only scratches the surface of how tech leaders approach AI security and risk management. For a more comprehensive view, check out our full report on  Trust, Risk, and Security Management in AI . You'll find deeper insights into how organisations build trustworthy AI systems and where critical gaps still exist. You can also explore AI and related topics more on SlashData’s blog . Questions or feedback? We’d love to hear from you. Whether you're looking to collaborate, dig into our data, or simply want to chat, feel free to contact us. About the author Bleona Bicaj, Senior Market Research Analyst Bleona Bicaj is a behavioral specialist, enthusiastic about data and behavioral science. She holds a Master's degree from Leiden University in Economic and Consumer Psychology. She has more than 6 years of professional experience as an analyst in the data analysis and market research industry.

Artificial intelligence (AI) is transforming the world of modern software development, with use cases that range from data processing to generating code. In this blog post, we explore the future of AI in software development from the perspective of professionals involved in software development who hold leadership positions [1] . We first consider how their opinions differ from their counterparts in non-leadership positions and then move on to breaking down their beliefs by company size and region. These insights provide a window into how the adoption of AI is evolving across the industry and where beliefs may diverge depending on organisational and geographical contexts. This blog post is based on data collected from over 4,500 technology professionals who answered questions about AI in the 28th edition of SlashData’s global Developer Nation Survey, which was fielded in Q4 2024. Looking for a broader business perspective? Discover how Sales & Marketing use Generative AI in our free report . Most important future use cases for AI in software development according to technology leaders When looking at the opinions of technology professionals in leadership roles and comparing them to those who work in non-leadership roles, we see a lot of broad similarities but also a selection of distinct differences. For instance, both groups show strong recognition of intelligent development assistants  (30% vs 29%) and data processing, analytics and visualisation  (26% vs 26%) amongst the most important future use cases of AI in software development. In terms of differences, we find that technology leaders are significantly more likely than their counterparts to emphasise the importance of AI in the future of cybersecurity  (25% vs 20%). While this is likely due to the differences between these two groups in terms of the scope of their responsibilities, the popularity of this particular use case points to AI playing a critical role in enhancing security against an ever-growing landscape of threats. Technology leaders are also more likely to believe in the future importance of areas such as AI for DevOps  (22% vs 18%) and predictive project management  (16% vs 13%), highlighting their focus on optimising workflows and managing their teams. However, they are less likely than those in non-leadership roles to consider code generation  (25% vs 29%) as important. This suggests that those who work closer to the code are more likely to see immediate benefits from automating coding tasks. Most important future uses cases of AI in software development by company size On looking closer at what technology leaders think, we find an interesting set of patterns when segmenting their beliefs by company size. While certain trends remain the same, our findings also show that the future importance levels of some use cases are perceived very differently across different company sizes. In terms of similarities, there is little variation in the perception of use cases, such as intelligent development assistants,  when we consider company size. This suggests that technology leaders expect future AI tools targeting such use cases to be just as useful for developers who work for small businesses as those who work for larger firms. This also points to a potential shift in the dynamics of the developer workforce as a whole, where developers can take on more strategic roles and focus on the bigger picture while leaving routine coding tasks to AI. We find that leaders who work for large companies are significantly more likely than average to place emphasis on code generation  (35%) when considering the future of AI in software development. This suggests that larger companies see greater potential in using AI-generated code in their applications. This may be because these companies often have extensive codebases that require a lot of developer resources. Large companies may be the most likely to use AI-generated code in the next three to five years Similarly, we see that the perceived importance of AI for cybersecurity  is strongly linked to company size. As businesses grow, they also increase the attack surface of their systems and require more complex security measures. This is reflected in our data, with technology professionals in leadership roles at large companies being the most likely to mention cybersecurity  (31%) in their beliefs of the most important use cases. This drops to 27% amongst technology leaders at midsize companies and further down to 20% at small companies. This suggests that smaller businesses may be less likely to prioritise advanced cybersecurity solutions when considering AI. Most important future uses cases of AI in software development by region Regional differences in culture, regulations and socio-economic circumstances often play important roles in technology. As such, it is no surprise that these differences extend to the opinions of technology leaders about which use cases for AI in software development will be most important in the next three to five years. As with the case of company sizes, some use cases receive similar favourability from technology leaders across Europe, North America and the Rest of the World. This suggests that certain use cases, like intelligent development assistants  and performance monitoring and optimisation , show universal promise of addressing challenges and opportunities in the landscape of modern software development. The benefits of intelligent development assistants are perceived to be not only company-size agnostic but also region-independent. Technology leaders working in Europe are the most likely to perceive cybersecurity  as one of the top use cases for AI in the near future of software development (30% vs 26% in North America and 21% in the Rest of the World). While this is partly due to Europe having an above-average concentration of large companies, it also highlights the greater emphasis placed on topics such as data protection in this region due to regulations. Despite this, these technology leaders also recognise the potential that AI has to bring to their future applications. In fact, technology professionals in leadership roles working in Europe are significantly more likely than their counterparts in other regions to believe that adding AI functionality to applications  will be amongst the most important future use cases of AI (25% vs 19%). Furthermore, we also see that they are disproportionately more likely to consider bug detection and fixing  as important than their counterparts in other regions (27% vs 20%). Key takeaways Technology leaders foresee AI playing a crucial role in software development, with strong recognition of intelligent development assistants and data processing. They also emphasise cybersecurity, AI for DevOps, and predictive project management more than those in non-leadership roles. Leaders in larger companies are more likely to believe in the growing importance of code generation and cybersecurity in the future. We also see some interesting regional differences, with European technology leaders placing a higher emphasis on cybersecurity, adding AI functionality to applications, and bug detection/fixing. These insights highlight the evolving adoption of AI in the industry and varying favour based on organisational and geographical contexts. What type of AI data are you looking for? Maybe we already have what you need. Get in touch with us. [1] We consider those who self-identify to be in at least one of the following roles as technology leaders: “tech/engineering team lead”, “CIO / CTO / IT manager”, “CEO/management”. About the author Nikita Solodkov, Market Research and Statistics Consultant Nikita is a multidisciplinary researcher with a particular interest in using data-driven insights to solve real-world problems. He holds a PhD in Physics and has over five years of experience in data analytics and research design.

The Internet of Things (IoT) is transforming how we interact with technology and the world around us. This blog post explores the key players driving this transformation - companies building in the IoT space. We will examine the regional distribution of IoT professionals, analyse how organisations participate in the IoT supply chain, and conclude by focusing on Industrial IoT and the markets these companies are focusing on. The data in this blog post comes from the 27th edition of SlashData’s global Developer Nation survey, fielded in Q3 2024. This survey gathered responses from over 7,500 technology professionals, including more than 900 individuals professionally involved in IoT projects - over 50% of whom are decision-makers - spanning both Industrial IoT and Consumer IoT. If you want to look at the general IoT developer population, you can have a look at our full report .  Where IoT professionals are located Before exploring how organisations engage in the IoT supply chain, we’ll first look at the regional distribution of IoT professionals to provide a general context that can help understand IoT industry dynamics. The professional IoT ecosystem is heavily concentrated in North America and Western Europe, which together account for 55% of the world’s IoT professionals. This concentration is likely due to the presence of mature ecosystems, advanced infrastructure, numerous market leaders, and strong business networks in these regions. North America leads the pack, hosting nearly one-third (30%) of the global IoT workforce. North America and Western Europe together account for 55% of the world’s IoT professionals When compared to the broader technology landscape, North America and Greater China stand out as hotspots for the IoT industry. The concentration of IoT professionals in these regions surpasses the concentration of all technology professionals by at least five percentage points, indicating a stronger interest and focus on IoT. In contrast, South Asia presents a different scenario. Despite being home to 17% of the world’s technology workforce, the region accounts for only 9% of IoT professionals. This disparity could be attributed to infrastructure limitations, skill gaps, or market dynamics that prioritise other technology sectors over IoT. Deep dive into the dynamics of the IoT supply chain The digital backbone Supplying software solutions and operating IoT services are the most common ways organisations participate in the IoT supply chain, with 28% and 26% of professional IoT developers engaging in these activities, respectively. Both activities form the backbone of the digital side of the IoT ecosystem and are deeply interconnected. Software suppliers create the platforms and tools that enable IoT systems to function, while IoT service operators leverage these platforms to deliver solutions such as connectivity management and device monitoring directly to customers. Likely due to this close collaboration and interdependence, we observe that many organisations involved in these activities are leveraging their synergies to expand their value propositions. According to our data, approximately one-third of software suppliers are also operating IoT services and vice versa. Completing the digital side of the IoT chain are network operators, engaging 19% of IoT professionals, who provide the connectivity infrastructure that enables IoT devices to communicate and exchange data, acting as bridges between physical devices and digital platforms. Similarly, we also observe that many network operators are diversifying their offerings within the digital IoT space, extending beyond infrastructure services. According to our data, at least one-third of IoT professionals working for network operators are also engaged in providing software solutions (40%) or operating IoT services (33%). The device side On the physical side of the IoT supply chain, Original Equipment Manufacturers (OEMs) lead the pack. 19% of professional IoT developers work for organisations that design, develop, and market products under their own brands. Other manufacturing-related activities –those performed by EMSs, CEMs, OCMs, and ODMs– each account for 15% or less of organisations in the IoT ecosystem. However, when accounting for overlaps, we find that about half (52%) of IoT professionals are engaged in manufacturing or design activities, closely mirroring the 54% involved in the digital side of the IoT chain. This near-parity highlights how hardware remains just as integral as software and services in shaping the IoT ecosystem. 19% of IoT professionals work for OEMs, which design, develop, and market products under their own brands. Similar to the digital side, we find strong synergies between different manufacturing activities. Many companies engage in multiple activities to capitalise on operational efficiencies and expertise. For example, 31% of Original Design Manufacturers (ODMs) are leveraging their design capabilities to produce their own branded products, effectively becoming OEMs, while continuing to create custom designs for other customers. Similarly, 30% of Contract Electronics Manufacturers (CEMs) are combining their contract manufacturing capabilities with in-house product development. Despite the overlaps and synergies observed across both the digital and physical sides of IoT, fully integrated organisations (those managing all aspects of design, manufacturing, software development, and service delivery) remain relatively rare, with only 11% of IoT professionals working for fully-integrated businesses. This suggests that most companies prefer to leverage synergies within closely related areas rather than taking on the complexity of full vertical integration. By focusing on adjacent activities, companies can diversify revenue streams and reduce reliance on a single business function while maintaining operational focus and avoiding the challenges associated with managing end-to-end operations. The services side  Beyond the core building blocks of the IoT ecosystem (services and devices), technical consultancies (22%) hold a notable presence in the IoT supply chain. This likely reflects the complexity of IoT deployments, where organisations rely on external expertise for solution design, system integration, and implementation strategies. Lastly, at the bottom of the chart, we find value-added resellers and distributors, accounting for only 10% of IoT professionals. These entities play a crucial role in bridging gaps between hardware manufacturers, software providers, and end-users by customising solutions to meet specific needs. Markets targeted by Industrial IoT professionals Now that we understand how IoT professionals participate in the IoT supply chain, let’s examine the markets they are currently targeting. For the purpose of this blog post, the analysis focuses exclusively on Industrial IoT (ΙΙοΤ). If you want to explore more insights on consumer IoT, get in touch. We observe a strong inclination towards industrial and infrastructure-related markets, likely driven by IoT’s ability to enhance operational efficiencies and reduce costs in these areas. Manufacturing is, by far, the most commonly targeted sector, with 35% of IIoT professionals focusing on it, likely driven by the shift towards smart factories under the Industry 4.0 movement . The second most targeted market is smart cities and infrastructure, attracting 24% of IIoT professionals. This highlights the growing role of IoT in urban development, supporting applications such as traffic management, waste management, and public safety systems. Following closely behind is a diverse set of markets, each targeted by 16% to 20% of IIoT professionals, highlighting the versatility of these solutions. Environmental monitoring (20%) leads this group, likely driven by sustainability initiatives and increasing regulatory requirements. Lastly, the least targeted markets include hospitality and tourism (13%), retail (12%), and defence (7%). While these sectors leverage IIoT for specific applications such as customer experience enhancement or security, they remain less attractive to IoT professionals, likely due to lower overall demand or fewer opportunities to effectively leverage IoT in these markets compared to others. Are you involved in IoT? Or simply curious about IoT market analytics? This blog post is just a glimpse into the demographic and firmographic insights of IoT professionals that we can offer. For a deeper dive into the world of IoT, we have a wealth of additional data and insights waiting to be explored. Get in touch , and we can talk about the details. About the author Álvaro Ruiz, Research Manager Álvaro is a market research analyst with a background in strategy and operations consulting. He holds a Master’s in Business Management and believes in the power of data-driven decision-making. Álvaro is passionate about helping businesses tackle complex strategic business challenges and make strategic decisions that are backed by thorough research and analysis.

The Developer Program Benchmarking is SlashData’s semi-annual market analysis, on usage and developer satisfaction, of industry-leading developer programs. Market-leading developer programs, benchmarked The Developer Program Benchmarking is SlashData’s semi-annual market analysis, on engagement and developer satisfaction, of industry-leading developer programs. SEE A PREVIEW What Developer Program Benchmarking will do for you Developer Program Benchmarking helps you: Understand what’s important to developers Discover where you are over or underspending your budgets Evaluate how you Support, Engage, and Market to developers Understand best practice leaders Who’s has the best program features Benchmark your marketing strategy against competitors What we track Documentation & sample code Development tools, integrations & libraries Tutorials & how-to videos Training courses & hands-on labs Webinars & online interactive coding events Professional certification Internships & career support Answers in public forums (e.g. Stack Overflow) Official forums Technical support Mentoring 12. Feedback channels (e.g. issue tracker, developer advocate) 13. Information via newsletters, blogs, and social media 14. Meetups 15. Hackathons & contests 16. Conferences & trade shows 17. Access to devices & hardware 18. Early access program 19. Business assistance & funding 20. Marketplaces for software 21. Interactive learning environments 22. Community A full preview of the latest analysis is available SEE THE PREVIEW What it answers The Developer Program Benchmarking answers questions such as: How do the leading programs compare in terms of adoption, engagement, and developer satisfaction? Which forms of support do developers value the most and expect tech vendors to provide? What are the strengths and weaknesses of your competitors’ developer programs? What are the gaps that you need to address in order to improve your developer program? Developer Program Benchmarking Survey Understanding meets benchmarking Understand how your program compares with the industry or competitors Understand how your community compares with the industry LET'S TALK

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