A few years back, a junior manager from a well-regarded international manufacturing company contacted me. He said, “My VP wants me to develop a digital transformation strategy for the security department.”
“Great,” I replied, offering my assistance. “Can you provide some details? What are your objectives, and what’s the general plan for developing and implementing this strategy? Also, how much time do you have?”
“You have a week,” the manager replied, reflecting the daunting task ahead.
“Well, that’s a problem,” I said. “A well-articulated digital transformation strategy involves aligning stakeholder expectations, creating a unified vision, and developing a multi-year implementation plan everyone understands and supports. Achieving this in a week is virtually impossible.”
The manager, determined to meet the impossible deadline, gave it his all. But the sheer complexity of the task proved too much, and he resigned within a couple of months.
At that time, digital transformation (DT) was a hot topic within the security community. Many companies launched DT initiatives, usually as an IT strategy and program. This left many security departments scrambling to understand what DT was, define its relevance, and determine how physical security fit into their company’s broader DT initiatives. Some companies successfully adopted a systematic planning process with a clear vision, while others missed the mark. However, with the right approach and understanding, digital transformation can lead to significant success.
Evolution of Digital Transformation in Security
Digital transformation within the corporate security world is well underway, albeit somewhat hidden. The focus has partially shifted from treating DT as a structured process with planned outcomes to grappling with a surge of emerging, paradigm-shifting technologies and figuring out how to integrate these new options and capabilities into security programs. This influx of technologies significantly complicates decision-making. Five years ago, aligning with corporate digital transformation goals was crucial for security departments. Today, it is even more vital with the rapid rise of artificial intelligence (AI), the Industrial Internet of Things (IIoT), third-party cloud hosting, security drones and robots, and advanced detection technologies. Cybersecurity concerns from malicious AI usage and new network and application security models introduce further complications. These emerging technologies are dynamic, developing capabilities at unprecedented speed and technically distinct from traditional security solutions, thus adding to the complexity.
Digital transformation traditionally – though now a largely outdated definition – refers to migrating analog or physical objects and manual processes into digital ones, fundamentally changing how organizations operate and deliver value to stakeholders. Within physical security, this transformation has historically involved transitioning from analog to digital systems, such as moving from traditional analog CCTV to IP-based video surveillance, security guards using security operations applications, and adopting advanced integrated access control systems.
Today, major DT implementations include improved data analytics for enhanced reporting, AI-enabled systems, including drones and robots, and cloud-based solutions to improve operational effectiveness and facilitate timely decision-making. Many companies, however, maintain understandable secrecy around their DT initiatives. As a result, organizations considering physical security DT initiatives – even if not naming it digital transformation – often ask their industry peers, “What are you doing in AI/IIoT/drones-robots/advanced integrations/cloud?” to benchmark their ideas against industry practices, rather than solely relying on published literature.
Emerging technologies like AI and more established ones like IIoT and cloud computing continue to drive significant changes in the physical security landscape. For example, AI already plays a pivotal role in enhancing surveillance and threat detection capabilities. By analyzing vast amounts of video data in both historical and real-time, AI algorithms can identify suspicious activities, recognize specific people, if permitted, and detect anomalies that human operators might miss. This improves the accuracy and efficiency of surveillance and enables proactive threat management.
Integrating IIoT into Physical Security Frameworks
Meanwhile, IIoT can significantly enhance the capabilities of traditional security systems by enabling broad and diverse data collection and real-time monitoring across various devices and sensors. For instance, IIoT devices can be integrated into access control, intrusion detection, and video surveillance, creating a unified platform and providing a total and detailed enterprise situational awareness snapshot. These integrations allow for immediate response to incidents, predictive maintenance of security devices, and better resource allocation based on real-time data.
The value of IIoT as a data input device can’t be overstated. Still, it is critical to note that adding IIoT devices to a security platform requires planning outside the physical security domain. SCADA isn’t usually tightly coupled into physical security systems per se, other than many SCADA controls are monitored by video surveillance. However, shared IIoT devices and data broaden the attack surface for SCADA and physical security; hence, thoughtful use-case development and cybersecurity design are critical.
An excellent and emerging security data source is Light Detection and Ranging (LiDAR), a technology first developed in the early 1960s that has significantly expanded its commercial applications, primarily due to advances that have enabled the components to become solid-state, mass-produced, and thus more affordable. While LiDAR is widely recognized for helping security drones and robots navigate during travel, it also offers a valuable solution for spatial monitoring in environments where traditional video surveillance may raise cultural sensitivities or operational concerns.
For example, LiDAR sensors can be strategically placed in the common areas of buildings to monitor people's movement and identify areas where crowds are forming. This application proves particularly beneficial in scenarios where video surveillance is considered intrusive or culturally inappropriate, as LiDAR sensors do not capture visual images of individuals but rather generate anonymous data maps that respect privacy while ensuring security. LiDAR sensors can be integrated into a physical security system via common APIs, from a basic interface to full sensor integration with live-view monitoring.
LiDAR is just one example, but many, including sound detection sensors and select environmental sensors such as gas, water/flood, and temperature/humidity, can be integrated into a security system. Indeed, none of these is new, but the ability to integrate them and exploit their utility is becoming easier with AI-enabled integrations. The ability for an AI system to write advanced integrations is just around the corner, making security systems more than just a utility system, as some companies currently view them.
Advantages and Considerations of Cloud Infrastructure for Physical Security
The migration of physical security systems to the cloud as a digital transformation strategy presents a viable option for certain organizations seeking enhanced flexibility and scalability. While certain physical security components, such as access control and surveillance systems, can be migrated from on-premises to cloud-based platforms, there are notable constraints and challenges. One significant issue is the massive data volumes generated by continuous video surveillance, which can strain bandwidth and increase storage costs. Consequently, a blended on-premises mass video storage and cloud-based incident video is worth considering. Additionally, the handling of sensitive biometric data in the cloud raises serious privacy concerns, necessitating stringent compliance reviews and compliance with data protection regulations.
Moreover, the shift to cloud-based physical security systems potentially expands the cyber-threat landscape. The increased use of APIs for integration can lead to vulnerabilities, making systems more susceptible to cyber-attacks, including DDOS, data leakage, and account hijacking. However, cloud services offer high-security hosting strategies that can safely host security applications but with higher incremental costs. However, information security risks posed by potential insider threats, where unauthorized access within the organization could lead to significant security breaches, are still a concern. As such, while Physical Security as a Service is gaining traction among some corporate users due to its operational benefits, it is not a one-size-fits-all solution for all aspects of physical security.
The Expanding Role of AI in Enhancing Security Operations
Not since the advent of IP-based security systems has another technology so profoundly enhanced the value delivered by physical security systems as Artificial Intelligence. AI is reshaping the landscape of physical security, moving beyond the traditional scope of basic video analytics and into more systems. Today’s video analytics, for instance, achieve near-human levels of pattern and object recognition but with a consistency that human operators cannot match, operating with attentiveness 100% of the time. The capabilities of AI in enhancing surveillance—such as enabling real-time threat detection and behavioral analysis—are just the beginning. Emerging applications are paving the way for a transformative era in security technology. Indeed, at a recent AI tradeshow, one AI developer, using computer vision AI, developed an algorithm that can calculate an aerial object's height and flight direction, making earlier drone detection much easier.
Advanced AI development for access control and intrusion detection systems is well underway. Existing security technologies such as shot detection systems (acoustic), and biometrics such as fingerprint and facial recognition will only improve with further AI refinement. Still, advanced AI is enabling new capabilities for our systems. For instance, developing AI “software agents” will enable systems to perform basic alarm analysis and preliminary response.
Imagine this scenario: An access control system with a virtual agent receives a door-forced alarm. The AI acknowledges the alarm and immediately connects to both interior and exterior cameras, monitoring the door to begin video alarm verification. Initially, the cause of the alarm is unclear, and the activity is logged. Despite this, the door-forced alarm repeatedly activates. Concluding that the door contact is likely faulty, the AI takes two actions: It alerts a SOC operator about the malfunction and then generates a maintenance request to add to a service queue. All that’s required from the operator is to confirm the request initiating the corrective action. Meanwhile, the AI continues to monitor the door for any actual forced entries, holding off on further escalation until repairs are made.
However, should an intruder attempt to exploit this door while it’s awaiting repair, the AI, upon detecting the door-forced alarm, will continue to verify the alarms via video verification and escalate the situation to a human operator. This allows security personnel to focus solely on genuine threats, ensuring that only valid alarms require human intervention and response.
These advanced algorithms can also automate risk assessment documentation, assist in personnel training (AI-based training, for example), and offer a level of linguistic and cognitive processing that closely mimics human intelligence. One established security software vendor is experimenting with AI to auto-generate integration scripts to connect disparate systems together via their respective application program interface kits, potentially upending traditional application engineering. Additionally, AI is increasingly deployed in security robots and drones, enabling them to perform autonomous patrols and make real-time decisions about potential threats. These devices can navigate complex environments, identify unauthorized activities, and respond to security breaches immediately without direct human oversight.
Convergence of Physical and Cybersecurity through Digital Transformation
Digital transformation in physical security and information/cybersecurity systems is pivotal as technologies evolve and threat landscapes expand. A holistic approach to security, integrating both physical and cybersecurity systems, is essential now more than ever. AI-enabled, unified security management platforms that combine data from physical devices with cyber threat intelligence can offer a more comprehensive view of security risks, especially regarding operational technology and IIoT. This integration will allow for sophisticated data analytics and real-time domain threat management. This has been on the horizon for a while, and maybe security’s “idea whose time has come.” The convergence of physical and cybersecurity calls for integrated threat management strategies and cohesive security policies. Training programs must evolve to equip security professionals with the skills to manage new technologies and complex threat environments effectively. This integrated approach ensures robust, responsive, and adaptive security systems capable of facing modern challenges.
Blueprint for Success: Structuring the Digital Transformation Process for Success
In the rapidly evolving landscape of physical security, a successful digital transformation (DT) process is crucial for evaluating established and emerging technologies, such as the Industrial Internet of Things (IIoT), cloud computing, AI, and even security robots and drones. A structured DT process must be implemented to harness these advancements effectively, guiding the security department through a comprehensive evaluation and integration strategy. This process begins with thoroughly assessing existing security infrastructure, identifying areas where new technologies can provide significant enhancements based on use cases, and a solid business case with justification and an ROI argument.
For instance, IIoT devices can offer granular, real-time data collection across various sensors and security checkpoints. At the same time, cloud computing facilitates scalable, on-demand storage and processing capabilities, enhancing data management and accessibility. Meanwhile, AI-driven robots and drones extend physical surveillance capabilities, responding dynamically to security incidents. Each of these technologies must be evaluated not only on its standalone merits but also on how seamlessly it integrates with existing systems and each other, ensuring interoperability and the creation of a cohesive security ecosystem.
A successful DT process In the security department also involves stakeholder engagement at every step, from conceptualization to deployment, ensuring that all potential tools are aligned with the overarching security goals and business objectives. This includes setting clear metrics for success, such as improved response times, reduced workforce costs, and enhanced incident resolution rates. Moreover, the DT process must consist of rigorous testing phases and regular reviews to adapt to new threats and incorporate feedback.
Ultimately, the key to a successful DT process in security is not just about adopting the latest technologies but creating a strategic, informed approach that leverages these tools to their fullest potential while maintaining system integrity and compliance. By doing so, security teams can ensure that their digital transformation leads to a more robust, efficient, and proactive security posture.
