5 Data Analysis Applications For The Security Industry 

5 Data Analysis Applications For The Security Industry

As business leaders and commercial organizations around the world continue to develop increasingly sophisticated, intelligent and tech-heavy security solutions, a growing reliance on the use of big data can be observed in the security industry. In fact, recent data suggests that the number of Internet of Things (IoT) devices in use worldwide is forecast to almost triple in size to around 29 billion by 2030. 

This metric is important as it helps to illustrate the popularity and efficacy of using smart, inter-connected technologies to manage modern security devices such as access control, surveillance cameras and alarms, with each of these systems now capable of collecting massive amounts of data. 

For modern security teams, the ability to not only view system data to better understand past events but also analyze this information to identify and combat novel threats is invaluable, with one industry study revealing that 94% of security professionals wish to use more data analytics in their work. To discover how teams can achieve this, here are 5 data analysis applications for the security industry. 

1. Cyber threat intelligence 

The frequency and severity of sophisticated cyber-attacks has been steadily increasing in recent years, with reports stating that recorded instances of global cybersecurity breaches rose by 38% in 2022 alone. Businesses across most major industries agree that more should be done to prevent cybercrime, with 78% of SMBs intending to increase cybersecurity investment in the next 12 months. 

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Intelligent data analysis can help security teams to better protect organizations from hacking attempts and malicious cyber activity by analyzing system data collected across connected networks. Rather than depending on human operators to manually monitor and detect subtle indications of attempted cyber-attacks, system-use data can be analyzed automatically to warn staff of suspected anomalies. 

By analyzing how essential systems are commonly used by authorized employees and guests, smart technology integrations can be developed to automatically respond to suspected breach attempts, with big data analytics allowing for a holistic security approach able to identify less obvious exploits.  

2. AI-powered video surveillance 

As the power and efficacy of AI-powered technology continues to grow, security professionals have been able to develop proactive AI-informed software integrations capable of dramatically improving installed video surveillance systems. Modern AI-powered surveillance cameras are now able to detect potential physical security threats based only on pre-programmed triggers informed by data analysis. 

Software integrations powered by data analytics programs can be used to automatically notify remote security monitoring teams of an unfolding situation if certain criteria are met, for example, a license plate camera may be instructed to send a remote alert if a certain vehicle is captured in live footage. 

Security officers can then immediately respond to the incident once the threat has been verified, with additional IoT integrations and cloud-based monitoring and management systems allowing teams to remotely lock access points or trigger on-site alarms. Utilizing video analytics systems in this way is particularly effective as multiple feeds can be monitored simultaneously to ensure that no threats are missed. 

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3. Access control management  

Another incredibly effective application of data analysis in the security industry is to improve the operation, maintenance and management of installed access control devices. For security teams tasked with monitoring comprehensive access control systems installed in large properties or spread across multiple sites, identifying individual faults and potential exploits can become a difficult task.

By leveraging data analytics programs to analyze user data collected from IoT sensors and access control management systems, unexpected faults and anomalies can be automatically identified, with programmed alerts sent to security teams warning them of the incident and suggesting a suitable fix. 

Data analytics tools can also be used to monitor credential behavior, meaning if an access card belonging to one individual is used in an unexpected manner (like being recorded in multiple sites at the same time), security teams can be immediately notified to revoke affected credentials remotely. 

4. Fraud detection systems 

Behavioral data analytics can also be used to help protect businesses from serious financial harm by intelligently analyzing data pertaining to monetary transactions. This concept is best illustrated by its use in fraud detection systems deployed by security teams working in financial services companies. 

By integrating data analysis programs alongside internal digital management systems, security staff can employ behavioral analysis techniques to identify and respond to suspected instances of credit card fraud. For example, if an unusual transaction is detected that doesn’t match any expected or previously recorded account data, active systems can be programmed to notify staff of the anomaly. 

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Data analytics practices can then be further deployed to compare the incident to historic data, acting to determine whether the account has been hacked, individual card details have been stolen or if a larger data breach is likely to occur, allowing teams to respond appropriately with immediate effect. 

5. Data forensics applications 

Data analytics programs are integral to the effective operation of converged security systems, whereby digital technologies and physical hardware devices are intrinsically connected and designed to communicate freely with each other. Converged systems allow IT and physical security staff to plan and monitor all security devices as a unified network to better respond to multi-platform attacks. 

For security teams to truly benefit from converged systems, data analytics programs must be utilized to turn vast amounts of recorded information into usable data, so data forensics practices are often leveraged to assist IT and security staff in investigating both historic breaches and ongoing attacks. 

Integrated data analytics software can be used to compare normal system data to specific information recorded during security breaches, helping teams to identify how certain systems were compromised and identify associated vulnerabilities that must be fixed to prevent similar incidents from occurring. 

Conclusion 

With businesses and organizations of all sizes becoming increasingly reliant on converged security networks, interconnected technologies and AI-informed systems, security teams and IT professionals are presented with vast amounts of valuable information that must be organized and understood. 

By utilizing a range of intelligent data analysis programs to identify behavioral patterns and subtle anomalies in recorded information, security teams can identify vulnerabilities and potential exploits much faster and with more efficacy than ever before, helping to bolster security systems across all industries.