Security Measures at the Intersection of Cloud and Artificial Intelligence (AI)
Security leaders must understand the value of their data sits at the core of integrating generative AI and cloud-native platforms.
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Global spending on public cloud services is forecast to grow 21.7% in 2024, which places emphasis on the need for cloud users to integrate generative artificial intelligence (AI) into their strategy to truly unlock all the data capabilities businesses need to operate in today’s tech environment.
Regardless of business priorities, security leaders must understand the value of their data sits at the core of integrating generative AI and cloud-native platforms to properly interpret and leverage this generated data to achieve the highest level of business value.
How is AI transforming decision-making processes for security professionals?
Simply put, AI is revolutionizing decision-making processes in physical security. It’s enabling security professionals to process and analyze vast amounts of data quickly and accurately—to detect patterns, predict potential threats, and provide real-time insights that inform smarter decisions.
On the smallest scale, the technology can be utilized in a home or small business, but at its largest it can be used to analyze the actions of thousands of individuals at once.
At large sporting events of tens of thousands of people, AI-powered video surveillance can be utilized to automatically identify unusual behavior or groupings, or left objects, allowing security teams to respond much faster than previously possible with human video surveillance professionals.
In tandem with this, in access control, AI can streamline processes by utilizing facial authentication for entry, and at the same time can also raise alerts when someone tries to gain entry by ‘tailgating’ after a valid access.
What are some common human errors in security management that AI is helping to eliminate?
AI is helping to minimize and even eliminate human error in security by not making the mistakes that even the most highly trained security expert can make. AI “on the edge” of security hardware can be trained to identify and assess critical details, interpret data accurately, and respond quickly to potential threats.
Security personnel may miss subtle changes in group activity or fail to notice unauthorized access attempts, especially when these tasks are required at scale, but AI-driven systems can continuously monitor and analyze these data streams, never losing concentration from tiredness.
How do AI-driven systems adapt to evolving security threats, and what role does machine learning play?
Machine learning (ML) enables AI-driven systems to continuously adapt to the organization’s needs. Gathering new data and learning from past incidents, ML can identify emerging patterns and adjust responses, accordingly, improving over time as they are exposed to more data.
This therefore goes beyond the threats that have been programmed into a system, making it a powerful tool for staying ahead of potential security risks. For our customers, an AI system might detect a new method of access control deception by comparing it to similar past incidents and adjusting its defense mechanisms in real time.
How important is cloud integration for powering data analytics in the security industry and what benefits does it bring to operational efficiency?
Cloud integration is crucial for powering data analytics in the security industry. It provides the scalability, flexibility, and storage capacity needed to handle large volumes of security data securely.
One of the changes we have seen in recent years is the effectiveness and safety of storing data in the cloud as opposed to on-premises storage, and customers are increasingly utilizing this method to store their security data, especially as network bandwidth to the cloud has improved.
By leveraging cloud-based platforms, organizations can process and analyze data from multiple sources simultaneously, enabling faster and more informed decision-making.
Cloud integration also allows for real-time monitoring and remote management of security systems, which enhances operational efficiency and reduces the need for physical infrastructure. This results in cost savings, improved response times, and a more resilient security posture.
What are some examples of how cloud-based data analytics have improved security operations in real-world scenarios?
In real-world scenarios, cloud-based data analytics have significantly improved security operations across verticals. For instance, universities are using cloud-native access control systems to secure campuses, allowing for immediate updates to credentials and ensuring that only authorized individuals can access specific buildings.
In healthcare, hospitals are leveraging cloud analytics to manage access control and monitor sensitive areas, enhancing patient safety and protecting valuable assets. In large manufacturing campuses, cloud-based video surveillance is used to monitor multiple locations simultaneously, improving overall security.
What challenges do organizations face when integrating cloud solutions into their security infrastructure and how can they overcome them?
We’ve worked with thousands of customers who have had concerns about data privacy, compliance with regulations, and the complexity of managing hybrid environments when integrating cloud solutions in their security infrastructure, but there are ways to overcome these potential issues.
Crucially, organizations should adopt a “data-first” approach, ensuring that all security data is properly encrypted and stored securely in the cloud. Additionally, they should work with cloud providers that offer robust compliance and security features and ensure that their IT teams are trained to manage the growing complexities of cloud-based systems.
We also promote the importance of interoperability in our systems, which is vital to ensuring seamless integration and reducing the risk of inefficient and costly vendor lock-in.
What best practices should security professionals follow when using AI-driven compliance tools to automate the management of security protocols?
Security professionals should follow several best practices when using AI-driven compliance tools, including regular audits and accuracy updates to the AI models. Transparency in these tools is crucial, to prevent AI appearing as an untraceable “black box” for users.
At all times, the management of these security protocols should be accountable. Security teams should also focus on continuous monitoring and testing of AI-driven systems to identify and address any potential issues early on. Additionally, adopting open standards and ensuring interoperability between different AI tools and platforms can help maintain a consistent and reliable compliance strategy.
How can AI-powered systems ensure the accuracy and reliability of security protocol management and regulation?
AI-powered systems can ensure the accuracy and reliability of security protocol management by continuously analyzing data and adjusting their algorithms based on real-time information. These systems use ML to improve their accuracy over time, learning from past incidents and adapting to new threats.
To maintain reliability, AI systems should be regularly updated with the latest security protocols and compliance requirements.
Although we are in an era of increasingly effective AI, human oversight remains important, to help catch any potential errors or biases in the AI’s decision-making process. Security protocols are managed effectively and consistently by safe and effective use of AI-powered systems by security professionals.
Rick Focke is director of product management, enterprise access control, for Johnson Controls Global Security Products.
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