5 applications of AI in the physical security landscape

The artificial intelligence (AI) revolution continues to pace across all aspects of business and IT. While many firms are waiting for product maturity and for AI tools to roll out to their applications of choice, others are exploring what is possible through easily accessible services like Microsoft’s Copilot, DALL-E and ChatGPT. Against this backdrop, concerns about privacy and security risks remain, as companies strive to harness AI's capabilities while mitigating its potential pitfalls. 

Around 9-in-10 security leaders agree that AI will have a major impact on physical security in the coming years. With AI augmenting the capabilities of security staff and improving role efficiency, the implications of this technology are significant for leadership and operators alike.  

The rise of AI, machine learning, and generative AI 

From Salesforce’s Einstein to Infor’s Coleman, many enterprise software vendors are implementing AI features, with aims to simplify tasks and add value for their users. Each arrival and update to these cloud tools adds to what they can help your business achieve. 

While some organizations or departments may be early adopters, others will take the wait and see approach. Meanwhile, a range of emerging technologies aimed at enhancing efficiency are being integrated into technology services, impacting various aspects of business operations: 

• Artificial Intelligence (AI) is the catch-all term for a set of tools and processes that allow machines to learn from inputs over time and adapt to new parameters without being explicitly programmed to. AI systems can range from simple rule-based systems to complex, self-learning algorithms. As such, each of the elements in this list can be considered subsets of AI. 

• Machine Learning (ML) is a branch of AI characterized by its ability to adapt its behavior over time in response to new data, often with minimal human supervision. When used in email security filters, ML algorithms can continuously “learn” from user feedback (such as emails being flagged as spam) and other data points to more accurately identify incoming spam emails in the future. 

• Deep Learning (DL) is a subset of ML that uses artificial neural networks with many layers (hence "deep") to learn complex patterns and relationships within data. Using large datasets, DL models can be trained to produce a desired outcome (like identifying objects within an image) and can be used for classification, such as speech recognition and natural language processing. 

• Generative AI (GAI) refers to a field of AI that focuses on generating similar styles of content or data based on patterns and examples from existing datasets. These models can be used to create new images, text, music, or even videos, in response to a given prompt. For example, a GAI model trained on a dataset of human faces could be used to generate images of new, realistic-looking faces.   

• Natural Language Processing (NLP) employs deep learning methods to interpret everyday written or spoken language, enabling interaction with a computer or program without the need for coding experience or an understanding of complex interfaces. For example, NLP powers virtual assistants on smartphones, allowing users to issue voice commands and receive appropriate responses. 

Each can help to support and simplify business and user operations in a number of ways.  

For instance, creative teams can harness GAI tools to streamline graphic design and video production tasks. With these tools, personalized graphics and stock videos can be generated quickly and efficiently, empowering teams to produce captivating content to suit specific audiences or niche applications. AI tools can also enable broader corporate outreach by offering automated language translation and text generation services, enabling businesses to cater their existing content to different audiences more effectively. 

In addition to content creation, AI can also make sophisticated technology easier to use for everyday employees. With NLP-powered tools and platforms, users can create workflows and automate tasks without the need for complex code. This democratization of automation allows a wider range of users to leverage the power of AI to streamline their daily tasks and processes. 

In this way, AI not only allows individuals to resolve their business needs more independently, but also makes complex tools and technology more accessible to those with less specific skills. This can reduce the amount of support they require from the IT department, which frees up time for those teams to focus on more delicate issues. 

The applications of AI in physical security systems 

1. Condensing information overload: In environments with numerous devices like those in the Industrial Internet of Things (IIoT), the volumes of data generated by multiple security systems, devices, and sensors can be challenging for security teams to manage. With the right AI features, this information could be condensed into concise reports and highlight anomalies out of the mass of data being collected.

2. Improved user experiences: The applications of AI in physical security lies also in its capacity to interpret the intent of users within the system. Natural Language Processing (NLP) features could allow users to simply speak or type their search queries into the search bar using everyday language, rather than technical jargon. The system could then interpret this request, offering clarifications if needed, before taking action. This would not only make security operations more intuitive and efficient, but also empower security teams to focus on their expertise, rather than navigating complex software interfaces. 

3. Enhancing forensic search capabilities: AI-enabled forensic search capabilities could be used to help identify and investigate suspicious activity. Machine learning algorithms are capable of sifting through vast amounts of data from various sources, including surveillance footage, access logs, and sensor data, helping users to reconstruct timelines and identify any irregularities. These tools could also assist security teams in obtaining insights not available in traditional reports, such as "who accessed the office after hours?" or "who has been entering restricted areas?". In this way, security teams could more easily identify suspicious behavior that might be indicative of identity card cloning and badge surfing attacks. 

4. Facilitating operations: Detecting anomalies remains paramount in security operations, especially when it comes to preventing potential threats and mitigating risks. Security systems equipped with advanced algorithms can continuously monitor vast amounts of data from surveillance cameras, sensors, and other sources in real-time. By quickly identifying potentially dangerous or threatening behaviors out of the noise of general activity, AI enhances situational awareness and enables security personnel to respond promptly to incipient threats. This proactive approach improves the overall security posture of the organization, ultimately safeguarding assets, personnel, and facilities. 

5. Maintaining the chain of evidence: In complex security environments, preserving the integrity of evidence is crucial for investigations and legal proceedings. AI can play a pivotal role in helping personnel to maintain the chain of evidence by automating the collection, storage, and analysis of data from various sources. When used in conjunction with digital evidence management systems and blockchain technology, AI can enable organizations to ensure trust, maintain chain of custody, and prevent tampering of surveillance footage or records by malicious agents, thereby safeguarding their business operations, assets, and reputation. 

Best practices for responsible AI and physical security integration 

The fact that AI never tires, blinks, or needs a break is one of the several reasons that organizations are embracing its applications in a security context. Yet when integrating AI and physical security, it's also important to implement Responsible AI practices: 

• Conduct a risk assessment to evaluate the potential impact of automating a specific process on critical systems or safety protocols. 

• Prioritize implementing AI into processes furthest away from critical areas to minimize any unforeseen issues or disruptions impacting essential operations.  

• Incorporate human-centered design principles to ensure that AI implementations align with empowering humans and keeping them in the decision loop while enhancing usability and user satisfaction.

• Integrate privacy analytics into AI systems to ensure compliance with data protection regulations and mitigate the risk of unauthorized access or misuse of sensitive information. 

• Establish robust mechanisms for data privacy and compliance, including regular audits and updates, to uphold legal obligations and protect user privacy. 

The integration of AI and physical security operations can also be facilitated by adopting an organized and collaborative approach to enhancing both physical and digital security measures. Combining IT and physical security efforts enables a unified approach to evaluating and mitigating digital and physical risks, thereby ensuring comprehensive security across both domains. This collaborative effort not only secures current operations but also prepares organizations to adapt to evolving security threats, including those that blend digital and physical elements.