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AI and Security: Trends for 2024 You Should Know

Ai and security trends for you should know

The constantly changing environment in the security industry is a primary focus of concern. As advanced technologies like AI, SaaS, and IoT continue to emerge, businesses must align their strategies with these trends. Failing to do so can result in falling behind in the technology landscape and, even worse, leaving vulnerabilities that pose risks and damage your company’s reputation. Below are some critical issues to be concerned with and key trends poised to reshape the industry.

AI in Security is the New Frontier, But it Comes with Risk

Navigating intricate networks in the last several years has meant integrating new technologies such as the Internet of Things (IoT) and access control.  However, along with these technologies comes untold risks and vulnerabilities. This complexity isn’t just a headache for those managing these systems; it’s a playground for cybercriminals, full of hidden nooks and crannies to exploit.

Artificial intelligence, with all its wonders, also has a double-edged sword. The very technology driving innovation could also empower cyberattacks with more intelligent, more adaptable malware or phishing scams, making the cybersecurity game all the more challenging.

But it’s not just about keeping out unwanted guests. The sheer amount of data IoT devices collect puts privacy in the spotlight, demanding stringent safeguards to protect our digital assets. Interestingly, the fusion of digital and physical security shows how intertwined our worlds have become, blurring the lines between a cyber breach and a physical break-in. It reminds us that securing our digital realm is as much about protecting our physical spaces as guarding bits and bytes.

One of the trickiest parts of this puzzle is the need for a universal playbook. The IoT world is a bit like the Wild West, with everyone doing their own thing, making it challenging to round up the bad guys. And let’s not forget the human element — sometimes the most significant risks come from within, whether a simple mistake or a malicious insider, adding another layer of complexity to the cybersecurity challenge.

Let’s look at the latest trends and the issues to address them.

AI and Large Language Models (LLMs)

Artificial intelligence, this year’s most talked-about subject, is at the forefront of these trends. Platforms like ChatGPT and Dal-e have evolved significantly over the last year, carving out indispensable roles in consumer and corporate environments. Their initial appeal, powered by capabilities in conversational AI and generative image creation, only scratches the surface of their potential.

Large Language Models (LLMs) are at the heart of these platforms, sophisticated algorithms capable of parsing and producing text with a startling semblance to human communication. This technology has proven to streamline complex decision-making, automating nuanced customer interactions, invigorating content creation, and fostering product innovation. LLMs adeptly navigate context, draw inferences, and generate articulate, relevant output, reshaping operational efficiencies and creative processes across industries, from composing intricate legal documents to formulating strategic narratives and advancing predictive analytics. People now see these platforms as more than just a simple tool; they are an integrated part of next-generation security, supply chain analysis, video analytics, and much more.

Generative AI and LLMs transform security operations, enabling quick, informed decisions in critical situations. Key innovations in applications such as data retrieval, anomaly detection, interactive training, and rapid response recommendations streamline command and control in security, heralding a new wave of efficiency.

The Double-Edged Sword of AI in Security: Opportunity and Risk

The scarcity of tagged security footage for AI training is addressed by synthetic data, exemplified by Nvidia’s virtual environments. These digital twins generate vast, accurate datasets, overcoming the traditional bottleneck of insufficient real-world examples and advancing AI’s capabilities in security.

However, generative AI’s ability to create realistic fake videos poses significant risks, potentially being used to fabricate security incidents and damage reputations. As AI integrates deeper into security systems, it’s crucial to balance its benefits with vigilance against its potential for misuse.

Leveraging AI in Security Measures and Business Operations

Leveraging AI in security measures and business operations offers transformative potential, with its ability to analyze large amounts of data, identify patterns, and make real-time predictive decisions. However, integrating AI systems into the security domain necessitates robust protection mechanisms to ensure the integrity and confidentiality of data. This involves implementing comprehensive encryption methods, regular audits, and secure data storage solutions to prevent unauthorized access and alterations. Additionally, AI systems must be designed with fail-safes and redundancy protocols to maintain operational continuity even during a cyber-attack or system failure, safeguarding critical security infrastructure and sensitive business information from compromise.

The ethical use of AI in security and business operations is paramount, requiring transparent and accountable AI frameworks that respect privacy rights and prevent bias. Organizations must adopt ethical guidelines and best practices for AI use, including developing explainable and auditable AI systems. This approach ensures that AI-driven decisions in security contexts, such as surveillance or access control, are made fairly and without infringing on individual liberties. Furthermore, ethical AI applications support corporate social responsibility initiatives, bolstering public trust in the organization’s commitment to ethical standards and the responsible use of technology.

To counteract the evolving landscape of cyber threats, AI systems must be fortified against attacks targeting AI, such as data poisoning and model evasion strategies. This involves continuously monitoring AI systems for anomalies that may indicate a security breach and implementing advanced cybersecurity measures tailored to AI’s unique vulnerabilities. Cybersecurity frameworks for AI should include regular updates and patches to AI algorithms, thorough vetting of data inputs to prevent manipulation, and collaboration with cybersecurity experts to stay ahead of emerging threats. By prioritizing AI’s security and ethical use, organizations can harness their full potential to enhance security measures and streamline business operations while upholding the highest data integrity and ethical practice standards.

This trend underlines the industry’s need to adopt comprehensive and proactive security strategies to safeguard AI technologies.

The Trend Toward Touchless Solutions

The move towards touchless solutions is not just a trend but a paradigm shift reshaping the security landscape in high-traffic areas. Touchless access control systems, characterized by advanced physical signal configurations, cutting-edge motion sensing technologies, and increasingly sophisticated mobile and biometric authentication methods, are setting new standards for safety and convenience in public and commercial spaces. This evolution is driven by minimizing physical contact and streamlining entry processes, making environments safer and more user-friendly.

Parallelly, the rise of remote management capabilities in security systems marks a significant advancement, particularly beneficial in today’s hybrid work cultures. This technology allows security administrators to manage access rights and adjust security protocols from anywhere, at any time, ensuring that the right people have the right access without compromising security. The integration of such systems offers unprecedented flexibility, enabling instant modifications to user permissions and access schedules without the need for on-site security personnel. This enhances operational efficiency and ensures that security measures can swiftly adapt to changing needs and situations.

Together, these advancements in touchless entry and remote security management are not just responses to immediate challenges but are indicative of the future trajectory of access control technologies. As these systems become more integrated and intelligent, they promise to redefine the standards of security, efficiency, and convenience in managing access to spaces, making them indispensable tools in future security infrastructure.

Providing Intelligence to Video Surveillance

Visual intelligence, powered by AI, is transforming traditional video surveillance into a more dynamic and insightful tool. The transition from mere surveillance to visual intelligence signifies the industry’s progression towards utilizing video data for broader applications, including operational efficiency and enhanced decision-making processes.

Generative AI and the regulation of AI are also highlighted as key trends. The rapid advancement and adoption of generative AI technologies, such as ChatGPT, necessitate a regulatory framework to ensure ethical use, transparency, and accountability. The impending regulations aim to foster a secure and trustworthy environment for AI deployment, balancing innovation with privacy and security considerations.

As cameras evolve into the ultimate sensing devices, they open new avenues for integrators to provide value to end users, transcending traditional security applications. This rapid pace of innovation is prompting the entire industry to elevate its game, meeting the demand for more consultative roles and implementing policies and regulations that ensure the responsible use of AI and large language models (LLMs).

This broadening scope of camera technology presents both opportunities and challenges. Integrators ready to enhance their consulting capabilities across a broader spectrum of functions stand to benefit significantly, maximizing the return on their client’s investments in surveillance systems. Conversely, those with a narrow focus risk missing out on more lucrative projects.

Similar to the manufacturing sector, integrators need to critically assess AI-driven use cases and establish clear boundaries for the types of applications they will engage with in their practices. While this might result in turning down specific opportunities, building a sustainable business model that avoids reliance on solutions at risk of becoming obsolete or banned, thereby protecting the company’s long-term reputation and success, is crucial.

Convergence of Operational and Information Technology

The merging of Information Technology (IT) and Operational Technology (OT) represents a significant transformation in the business and security landscape. This convergence, particularly at the edge where devices connect and interact, integrates the once separate OT systems into the broader corporate data ecosystem. Doing so unlocks the immense potential of the Industrial Internet of Things (IIoT) and offers valuable benefits through unified and interconnected systems.

However, this convergence creates a more complex network architecture, introducing higher cyber and physical security risks. As these systems become more interconnected and intricate, the security sector is crucial in mitigating these risks. Recent high-profile cybersecurity incidents like the Colonial Pipeline ransomware attack serve as reminders of the vulnerability and potential impact of threats in this interconnected environment.

Establishing robust standards for IT-OT convergence to address these challenges is important. These standards are critical in ensuring interoperability and strengthening cybersecurity defenses against evolving threats. Organizations can better protect their networks, data, and operations by implementing these standards.

Moreover, collaboration between IT and OT professionals is essential in successfully navigating this convergence. By working together, they can leverage their expertise to develop comprehensive solutions that prioritize security and functionality. This collaborative approach fosters a proactive risk management culture and enables organizations to avoid emerging threats.

Regulatory Trends Affecting AI

International efforts to balance innovation with risk management increasingly influence AI regulation trends. The EU’s pioneering AI Act introduces a risk-based classification for AI applications, setting stringent standards for high-risk uses and banning specific technologies like facial recognition in public spaces without court approval. This act aims for transparency in AI development and greater accountability for potential harms caused by high-risk AI systems.[1]

Despite the absence of comprehensive AI legislation in the US, agencies like the FTC are proactively addressing AI-related consumer protection issues using existing legal frameworks. Congressional efforts, such as the AI Insight Forums, are underway to educate lawmakers on AI, indicating a growing commitment to developing AI regulations.[2]

Globally, the regulatory landscape emphasizes core principles like human rights and risk management, a mix of sector-specific and agnostic rules, and the importance of international cooperation. There’s a focus on policy alignment with broader digital priorities, like cybersecurity and data privacy, and encouraging private-sector collaboration to navigate AI’s complexities[3].

The SaaS Integration Model

Modern security solutions are broadening their impact, offering businesses substantial value beyond safeguarding assets. This shift is particularly pronounced in access control, where data from these systems unlocks actionable insights, making them vital for operational efficiency and strategic planning.

Retail spaces benefit from analytics like heat mapping and customer flow, optimizing store layouts, and enhancing the shopping experience. In corporate environments, access control systems secure premises, gather data for real estate optimization and verify employee attendance. Remote monitoring capabilities ensure operational continuity and maintain standards across business facilities. Integrating security with building management systems, such as lighting and HVAC, further enhances efficiency and sustainability.

This expanded role of security technologies transforms the perception of security professionals within organizations. By demonstrating clear operational benefits and ROI across departments, security practitioners reinforce their status as key contributors to business success, transcending their traditional roles.

Your Next Move

For businesses navigating this complex landscape, the integration of IT and OT, coupled with the responsible use of AI, presents both opportunities and challenges. Companies need to stay ahead of these trends, ensuring their security systems protect assets while driving operational efficiencies and contributing to the bottom line.

To adapt to these changes and leverage your security systems’ full potential, consider contacting our team at NCS. We can evaluate your current and future technology needs and help you navigate the complexities of security to enable your business’s future success.

[1] https://www.technologyreview.com/2024/01/05/1086203/whats-next-ai-regulation-2024/

[2] https://www.natlawreview.com/article/three-trends-ai-regulation-2023-ai-washington-report

[3] https://www.ey.com/en_us/ai/how-to-navigate-global-trends-in-artificial-intelligence-regulation

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