An image illustrating the article accompanies the text. Several ongoing and emerging trends within the security sector are discussed. The enduring influence of AI applications within the sector remains prominent, necessitating consideration of new challenges. Alongside established trends, novel or resurgent topics are gaining traction.
Legislators are striving to adapt to rapid technological advancements, focusing on AI, cybersecurity, privacy, and resilience for critical entities. This pervasive regulatory landscape demands organizational responsiveness. These trends offer substantial opportunities for enhanced capabilities, flexibility, efficiency, and value for customers.
Hybrid architectures, leveraging edge, cloud, and on-premise technologies, have become the preferred choice for security solutions. Each organization’s architectural decisions are guided by unique technological, legal, ethical, and governance factors. The dynamic environment necessitates adaptability, making freedom of choice paramount. Hybrid solutions offer this flexibility in data management and device control, optimizing system solutions tailored to specific needs and scalability.
Rapid advancements in AI, particularly deep learning and generative AI, continue. While hype exists, practical applications of generative AI in security are emerging, presenting opportunities alongside ethical, legal, and corporate considerations. The computational demands of generative AI models raise concerns about cost, energy consumption, and environmental impact. Efforts to reduce model size without sacrificing quality are underway. The growing use of AI technologies further solidifies hybrid architectures as the norm.
Different AI types, from deep learning-based object recognition to generative AI, require deployment at various points in the value chain and specific environments. Generative AI, though requiring significant processing power, can enhance operator interaction with security solutions through natural language processing. Deep learning analytics, like object recognition, can be performed directly within surveillance cameras.
Generative models may eventually operate, at least partially, on cameras with high-quality results. Simultaneously, models are improving in ethical aspects, bias mitigation, hallucination reduction, and decision-making accuracy. This evolution holds significant potential for revolutionizing security operations’ efficiency and effectiveness. Algorithms will analyze diverse input data, including visual information from radar, audio, and other sensors, enabling proactive capabilities and generating valuable insights for long-term planning.
Advanced computer vision, audio, access control, and connected technologies continue to enhance security and safety. Increased accuracy in AI-powered analytics, particularly object recognition, enables faster and more effective incident response. Data generated by various sensors extend beyond traditional security applications, offering opportunities in operational efficiency and business intelligence. This trend fosters collaboration across organizations, as technology deployed for one purpose, like security cameras, can yield data for improving customer or employee experience, sustainability, or process efficiency.
Rapid innovation is fueled by high-quality hardware platforms. Open and collaborative ecosystems of application developers and system integrators provide significant value to customers. A renewed focus on image quality is driven by the shift towards computer-based analysis and continuous monitoring. Higher resolution images enhance analytics and AI applications, leading to more accurate object recognition and detailed data. This renewed focus on image quality drives efficiency, as fewer high-resolution cameras can cover larger areas, supporting analytics in complex environments. While humans remain integral to the process, automated alerts enhance operator efficiency. The importance of camera maintenance is underscored, as minor obstructions can significantly impact analysis.
High-quality security hardware increasingly comes with heightened expectations for longevity. Ongoing software support is crucial for maintaining functionality, cybersecurity, and lifetime value. Vendor commitments to long-term software support are vital for enhancing capabilities and ensuring optimal security. This impacts the total cost of ownership, as well as the cost of camera systems over their lifespan. Investments in high-quality cameras with comprehensive software support ultimately prove more effective and efficient.
Meeting customer needs is paramount. Technological innovation should align with end-user priorities, encompassing safety, security, operational efficiency, business intelligence, cybersecurity, sustainability, and flexibility through open standards. Technology vendors with greater control over their core technologies are better positioned to meet these requirements. This control enables the design of specific capabilities and functionality, reinforces “secure by design” commitments, and mitigates supply chain disruptions. The trend of software vendors designing their own semiconductors for enhanced control, particularly in AI, is expected to continue across sectors. Core technology independence, exemplified by in-house system-on-chip development, is a key differentiator.
These trends are anticipated to shape the security sector in 2025, prompting further discussion and insights from industry stakeholders. Johan Paulsson, CTO at Axis Communications, authored this perspective. The views expressed are solely his and do not necessarily reflect those of ETCISO. ETCISO bears no responsibility for any damages incurred directly or indirectly. The article was published on December 24, 2024, at 11:49 AM IST.
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