What new developments in AI analytics mean for the security sector

New developments in Artificial Intelligence (AI) and the increased adoption of AI tools have dominated the news cycle for the last few years. From reports claiming as many as 77% of businesses have started using, or are exploring the future use of, AI processes to enhance key operations to studies claiming 64% of businesses believe AI will increase productivity.

Much of the discussion surrounding the adoption of AI technologies at present seems to be centered on productivity and automation in the business world. But new developments in AI analytics are quietly, at least in comparison to workflow-focused developments, beginning to completely revolutionize the way organizations and leaders approach commercial security.

By providing security professionals the ability to develop automated incident responses and better-understand unique vulnerabilities through continuous machine learning, AI analytics in the security sector are assisting stakeholders in proactively addressing sophisticated threats.

To learn more about the potential for such technologies to ensure a safer and more secure environment for business and property owners, the following post looks to explore what new developments in AI analytics mean for the security sector in terms of tackling current threats.

Understanding the threat landscape

To understand the significant impact that sophisticated AI analytics tools are currently having on the security industry, it’s important to consider the threat landscape that modern security professionals are contending with. As a result of multiple contributory factors, ranging from socio-economic tensions to the increased adoption of digital systems, threat levels are rising.

According to a report published in 2022, almost 30% of organizations have experienced a notable rise in physical security incidents targeted towards their operations in recent years. This generalized image of the physical security landscape at present can be further broken down by focusing on the rise of specific threat types in recent times. Key examples include:

• Cargo theft: Cargo thefts across the US rose by almost 60% in 2023, with increasing reports of warehouses, trains and shipping containers being broken into by organized and opportunistic criminals. Experts believe theft volumes will rise further in 2024.
• Vehicle theft: The commonality of vehicle thefts has risen consistently across the nation since 2019, with some states experiencing a percentage change of almost 65% between 2022 and 2023 alone. In 2023, national figures increased by 25%.
• Internal theft: Insider threats are also impacting business operations at an increased rate, with data suggesting such threats to have risen by almost 20% in recent years. The average cost of an insider attack during 2022 was found to equal $17.19 million.

In addition to the above-mentioned physical security issues that modern organizations must contend with, sophisticated cyber-attacks are also becoming increasingly common. In 2022, the commonality of cyber-attacks rose by almost 40% on a global scale. With security teams facing an ever-expanding list of threats, many businesses are struggling to effectively adapt.

Harnessing AI analytics in the security sector

For organizations and hired security personnel to effectively address modern threats, teams must find reliable ways to both deter and instantly respond to suspicious activities. While, of course, the obvious solution to this problem is to increase the numbers of personnel visibly stationed at high-risk locations, doing so will rarely be considered cost-effective.

Instead, many organizations are exploring ways to enhance existing security teams’ abilities to observe key locations and identify anomalous behaviors. By developing bespoke AI tools programmed to continuously monitor security data, professionals can dramatically improve their ability to notice telltale signs of incoming security incidents before damage can be done.

The key to the benefits of AI analytics deployed in the security sector concerns the ability for these solutions to autonomously detect threat-adjacent stimuli. For example, dome security cameras equipped with AI video analytics software can be installed to cover high-risk areas, with AI tools capable of autonomously detecting suspicious activities and contraband items.

In a typical installation, detectable stimuli may include:

• Crowds forming in unusual areas
• The presence of weapons
• Motion in restricted locations
• The movement of high-value assets
• The presence of unknown vehicles

With continuously operational security devices covering the observation of high-risk locations, security personnel can improve the efficiency of their physical security duties. For example, If cameras are connected to a cloud-based management system, on-site security staff can access live feeds and receive real-time alerts to focus patrols on areas of concern.

This same principle of AI-informed security monitoring can be equally applied to many wider aspects of physical and cyber-security operations. For example, access control readers can continuously monitor access requests to warn staff of odd events, while cyber-security tools like endpoint detection and response solutions can perform similar tasks in the digital realm.

With all of these unique AI-informed solutions integrated into a wider security management system, security staff are suddenly provided a holistic view of all high-risk areas and assets. In practice, the development of such a solution helps organizations account for rising levels of criminality by providing existing security personnel continuous access to high-quality data.

The future of AI analytics for the security sector

With recent developments in machine learning enabling continuously operational systems to autonomously analyze and draw conclusions from live data, AI informed security tools could be on the brink of an intelligence breakthrough. In support of this, researchers at New York University recently proved AI models can learn from new data at a similar level to that of a human child.

If these deep learning processes are combined with developments in image comprehension via facial, motion and pattern recognition, and continued advancements in AI-powered data processing capabilities, AI analytics could be used to measurably improve security outlooks.

In practice, this would allow for the development of fully-autonomous threat detection and response solutions in both physical and digital environments, with the results of live AI data analytics processes used to trigger wider security tools and notify human security personnel.

Existing security technologies such as cameras, access systems, on-site alarms and digital security tools could be left to continuously monitor incoming data for signs of suspicious activity. With support from machine learning algorithms, normal activity could be understood by AI analytics tools, ensuring all anomalous events are instantly detected and addressed.

Considering AI analytics software can already be used to reliably detect vehicles, biometric identifiers and anomalous data entries, the development and implementation of AI-informed security systems could represent a key breakthrough in the combatting of rising threat levels.

With AI investment forecast to reach a staggering $200 billion globally by 2025, it’s safe to assume that businesses and institutions across most major sectors will continue to pursue new ways to integrate AI tools into essential processes. Accounting for the benefits afforded by AI-informed security systems, the commonality of such tools is only expected to increase.