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    How Security In The Workplace Is Evolving With AI

    How Security In The Workplace Is Evolving With AI

    The development and use of AI-informed systems has been one of the most talked about topics of recent years, from advanced text generation tools to automated data analysis programs, it seems almost every aspect of modern life is being optimized and improved by integrations with AI programs.

    While there are plenty of uses for recreational AI models, at present, some of the most impressive deployments of AI technology may have been observed in the world of business. In fact, research published in 2019 found that 37% of organizations were utilizing AI in some capacity, with more recent figures suggesting over 90% of leading businesses are currently invested in developing AI.

    In particular, the ability for AI systems to automatically scan through and analyze large pools of data is proving invaluable to business security teams, aiding staff in improving the efficiency of almost all essential processes. To explain this further, here’s how security in the workplace is evolving with AI.

    Automated decision making

    To appropriately protect modern businesses, security teams are tasked with overseeing and making intelligent use of massive amounts of collected data. The average team must reliably assess CCTV footage, access control logs, data gathered from on-site alarms and the operation of cybersecurity tools to ensure that the network as a whole is capable of preventing sophisticated security breaches.

    Making sense of all this data, including how each set may influence the next, can be an incredibly difficult task for even the most experienced of security professionals, which is why an increasing number of teams are developing AI integrations designed to analyze active systems in real-time.

    In practice, AI-informed software is used to spot anomalies in recorded data and relay that information to security teams via automated notifications, with additional options to trigger wider security devices in response to detected stimuli. This allows human teams to better manage their resources and focus on the more complicated aspects of their duties without needing to permanently monitor data feeds.

    Enhanced security functionality

    For the most part, the adoption of AI technology is not intended to replace existing security systems or personnel, rather these tools are being designed to enhance the functionality of traditional devices. For example, security teams can improve the operation of CCTV cameras through integrations with object and license plate recognition systems and software configured to automatically alert staff of potential threats.

    AI is also being used to improve the efficacy of trusted security protections such as multi-factor and biometric authentication processes. Machine learning algorithms are now able to analyze the unique characteristics of system users to add an additional layer of verification to existing access control systems, whilst also improving biometric data analysis to account for variations in reference material.

    These ideas can also be extended to the operation of connected IoT devices like security alarms and sensors, with AI software programs capable of understanding how each device is used on an average day and configured to automatically alert admins if an irregularity is detected that may be of concern.

    Predictive cybersecurity policies

    Of course, the utilization of AI technology is not restricted only to the improvement of physical security hardware. Intelligent machine learning programs are often deployed to prevent common cyber-attacks long before criminals can gain access to sensitive information, typically through predictive analysis. By providing AI-informed cybersecurity software with data gathered from previous breaches, systems can be designed to detect potential vulnerabilities and exploits present in business computer networks that hackers have successfully intercepted in the past, allowing staff to address issues preemptively.

    Additionally, as AI software tools can analyze data collected from multiple endpoints simultaneously, smart systems can be developed to detect abnormal behaviors associated with the beginnings of a suspected cyber-attack, allowing admins to respond immediately to potentially malicious activities.

    Equipment tracking systems

    With 74% of US companies currently using or planning to implement some form of hybrid work model in the near future, security teams must find a way to appropriately manage the issuing of company-owned equipment loaned out to employees working from home. Even if these laptops and smart devices are protected by cybersecurity tools, companies would rather avoid the cost of lost devices.

    Thankfully, AI software can be deployed to automatically track equipment and provide management teams with notifications if devices are misused or left in the possession of employees for longer than approved, with relevant user data stored and cataloged in a secure business management system.

    This same policy can be applied equally to the maintenance of company-owned equipment, with AI integrations designed to assess the functionality of active devices and programmed to automatically notify admins if a fault or potential security exploit is detected, as well as if software needs updating.


    New developments in AI technology look set to continue transforming most aspects of the modern world, with workplace security being no exception. By allowing AI programs to analyze and assess large amounts of collected security data, businesses can design proactive systems capable of improving the efficiency and efficacy of installed security devices, whilst helping human security teams to streamline their own workflows and improve incident responses to better protect employees and company data.

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