The healthcare sector is considered one of the most insecure sectors to cyberattacks and breaches of physical security. In 2023, more than 700 data breaches of over 100 million patient records were reported. Given that hospitals and clinics are in quickly digitizing and moving sensitive information, it is more important than ever to secure both patient data and physical assets. In this environment, artificial intelligence (AI) and machine learning (ML) are fundamentally changing the space by offering the most intelligent security, the fastest response, and the most adaptable security available.
Contemporary surveillance systems, embedded within progressive healthcare security systems, do not simply function as passive video recording devices. These modern systems utilize video analytics and AI in real time to identify atypical behavior, track people who enter a restricted area, and in some cases, anticipate a security event before it occurs. The integration of AI and ML into surveillance systems signals a shift from reactive to proactive security management in healthcare settings.
The Growing Threat Landscape in Healthcare
Healthcare facilities have their own distinct security challenges. Facilities that must protect patient files and high value medical equipment, can be a high stakes industry. Healthcare is more regulated than other sectors. Whether HIPAA or other rules or regulations, the industry must navigate their complex rules governing access control, physical security and integrity of data. Moreover, legacy infrastructure and limited or vulnerable security staffing often create vulnerabilities to bad actors.
Standard security measures at hospitals—i.e. guards, identification badge systems and basic video surveillance—cannot handle emerging and sophisticated threats, such as insider threats, ransomware, unauthorized access to secure areas and medical identity fraud. Emerging and sophisticated risks create new kinds of threats for which new, better solution-based strategies are necessary.
AI-Powered Surveillance and Threat Detection
AI has begun to play a meaningful role in protecting the healthcare industry through current technology. AI is now making its mark on surveillance technologies. Surveillance using AI can process the video feed and recognize abnormal deviation from the norm and even do it in real time, whereas ordinary cameras alone can’t. These machine-learning models have been trained on massive datasets so they can recognize normal behavior, then detect deviations from that behavior to flag it. For example, when someone enters a restricted lab after hours or someone is loitering in or near restricted areas.
These smart security systems for healthcare take it a step further by also using facial recognition and behavioral biometrics to instantly identify authorized personnel. When combined with electronic access and badge readers, AI can also generate a comprehensive picture of who is entering and exiting each area and compare activity to who has access.
AI can additionally be employed to recognize possible threats of violence or emergency situations in healthcare environments. By analyzing elements such as tone of voice, body language, and group movements, systems can provide security teams alerts to potential escalating events, such as either patient or visitor aggression toward staff or another patient. In addition, AI can provide alert for response protocols.
Machine Learning for Predictive Risk Assessment
While AI takes charge of real time monitoring, machine learning excels at analyzing historical data for hidden risk. Today in healthcare, security systems are collecting tons of data—from incident reports and access logs, to network activity and security footage. ML algorithms can analyze the data in order to identify patterns which may escalate to a future breach or vulnerability.
As an illustration, let’s imagine that a certain ward has experienced several occurrences of unauthorized entrance. In this case, ML can associate these incidents with the specific day and time of day, staffing levels, and badge use to help identify when and where the next unauthorized entry may occur. Moreover, by assessing access patterns across EHRs, machine learning models may also be able to identify abnormal patterns of behaviors, such as a nurse requesting access to patient records that are outside their unit.
With this predictive model, healthcare organizations may utilize their security resources more efficiently instead of waiting for an adversarial incident to occur before working to address a perceived threat. In addition to improving responses and overall experience, the predictive model will provide the security team with actionable intelligence as well as context if a potential threat is identified.
Enhancing Cybersecurity with AI and ML
Apart from physical threats, healthcare systems are high-value targets for cybercriminals who are interested in stealing medical data or holding systems hostage via ransomware attacks. In this situation, AI and ML also provide an important safeguard. Next-generation Firewalls, intrusion detection systems, and endpoint security solutions now contain machine learning algorithms to detect, in real time, IP traffic that looks like it could be phishing, or some other attack, or malware signatures.
AI-enabled cybersecurity systems learn from each attack attempt to improve their detection capabilities. AI does this without the need for manual updates or human intervention. AI can provide incredible value in situations where uptime matters most, and any downtime could result in loss of life, and do so with speed and flexibility.
AI also supports behavioral analytics in the cybersecurity realm. AI can watch what constitutes “normal” user behavior (i.e., when they’re logging on, what they’re looking at, or when they’re using files), and use ML to see if the behavior is deviating from what has come to be defined as “normal.” The software can alert an administrator, or may even be able to quarantine the affected systems or commence lockdown automatically to stop the defectors and prevent lateral movement across the network.
Challenges and Considerations
While the potential benefits are extensive, applying AI and ML to health security comes with challenges. Data privacy remains a significant challenge in relation to technologies such as facial recognition and behavioral monitoring. Healthcare organizations will need to take additional action in order to meet the privacy protection responsibilities established by statutes and voluntarily undertaken under applicable ethical guidelines in health-related research, in order to avoid both legal liability and embarrassment.
AI systems also require high-quality data. Bad data, misaligned data, or incomplete data can lead to false positives or threats that were missed. The deployment and maintenance of machine learning models need to be a continuous process, and human expertise will always be needed to both overhaul and interpret how AI is performing.
The Future of Healthcare Security
While still in the early stages of incorporating AI and ML into security systems for healthcare environments, the direction is plain as day. As threats are becoming increasingly intricate, and interconnected, security will also need to be able to keep pace. We will likely see greater use of AI-driven drones tasked with patrolling facilities, use of natural language processing to monitor internal communications, and even AI assistants helping to triage alerts involving security or compliance issues.
In the end, the emergence of AI and ML is transforming the healthcare security model from being reactive to a proactive, predictive, and dynamic one. By adding intelligence to surveillance systems, access control, and cybersecurity tools, the healthcare community can deliver improved environments for patients, employees, and sensitive information.
Key Takeaways
- AI and ML are transforming healthcare security systems by making them predictive and adaptive.
- Surveillance systems now use real-time video analytics, facial recognition, and behavior tracking to detect threats proactively.
- ML algorithms help identify risk patterns and predict future breaches using historical data.
- In fact, AI-driven cybersecurity solutions are fast in responding to digital threats, compared to more traditional methods.
- Possible benefit aside, the risk of data privacy and model accuracy should be managed diligently.