Physical Address
304 North Cardinal St.
Dorchester Center, MA 02124
Chris Ciabarra is the CTO of Athena Security.
California recently proposed to mandate automated weapon detection devices at hospital checkpoints by 2027. While this might seem like a local initiative, the challenges it addresses—rising violence and staffing shortages—are nationwide.
Automation offers relief by assisting with tasks like weapon screening, easing staff burdens and speeding operations. But automation has limits—it follows predefined rules and can’t adapt to unexpected threats.
It’s AI technology that I believe overcomes these limits.
AI doesn’t just automate; it analyzes, learns and adapts to new challenges. For instance, it can analyze unusual behavioral patterns, such as someone lingering too long in a restricted area, which a simple automation system might miss. And that’s just one aspect—there’s much more AI can bring to hospital security automation.
Specific Use Cases Of AI In Automating Hospital Security
With AI automation in our hospitals, we’re not just reacting to threats; we’re predicting and neutralizing them before they occur. This isn’t just security—it’s foresight, turning what once seemed like science fiction into everyday reality. See for yourself:
Automated Weapon Detection
Screening for weapons has long required multiple officers, one to manage traffic, and others to perform secondary checks when items are flagged. This approach is slow, labor-intensive and prone to human error. In fact, I have often heard stories of how bad actors exploit these busy hours to slip through unnoticed.
AI-powered X-ray machines now automate this process, scanning baggage and individuals to detect weapons or prohibited items with unparalleled speed and accuracy. These systems reduce manual checks, decreasing staff needed for routine tasks without replacing them entirely. Staff remain vital in high-traffic areas for responding to AI alerts, while in lower-traffic zones, automation can largely substitute for manual oversight.
Controlled Access With Badge Validation
Manually verifying visitor badges and managing access at multiple checkpoints is both tedious and prone to human error.
AI-driven systems streamline this process by automatically scanning badges and/or using facial recognition to validate credentials. This enhances staff efficiency, focusing their efforts on critical tasks, ensuring only authorized access to sensitive areas.
Compliance With DHS Guidelines
Non-compliance costs hospitals up to 3.5 times more than compliance, averaging $9.6 million in fines. Beyond financial penalties, failures risk reputational damage and loss of patient trust.
AI simplifies compliance with regulations, including those set by DHS, by monitoring security protocols, ensuring officers follow required procedures and validating restricted access to ensure only authorized individuals enter. It generates real-time alerts for breaches and automatically documents incidents, providing accurate records for audits.
Enhanced Video Surveillance
Monitoring large hospital areas for unusual behavior or potential threats is resource-intensive and prone to human fatigue. AI-powered surveillance systems assist by analyzing live video feeds in real time. These systems use advanced pattern recognition to identify suspicious activities, unattended objects or potential risks with precision.
When a threat is detected, AI immediately sends alerts to security teams, enabling quicker responses. By reducing reliance on constant human oversight, AI makes hospital surveillance smarter, faster, and more reliable.
AI can transform hospital security, but we need to address challenges like accuracy and data security upfront for seamless implementation.
Challenges with AI-Powered Automation in Hospital Security
When I speak with hospital authorities about AI, the first concern is almost always reliability: “Can we trust it to work accurately, treat everyone fairly, and stay compliant with regulations?” These concerns are valid—no one wants to implement a system that might unintentionally discriminate or invite lawsuits.
Take Illinois, for example. In the Chicago area, there’s a ban on facial recognition technology—not just because of privacy concerns, but due to potential biases in AI programming, that could lead to discrimination. And let’s be clear: This isn’t limited to facial recognition. Any AI system that infringes on individual rights or compromises data privacy can face similar challenges.
So, how do we address this head-on? It starts with training AI models on diverse, representative datasets to minimize errors and biases. Rigorous testing under real-world conditions is equally important to identify and correct potential blind spots.
And what about sensitive data, like patient or visitor entry/exit records? This is where edge computing comes in. By processing data locally within the hospital, edge computing reduces exposure to cloud vulnerabilities, keeping all information secure.
But the challenges don’t stop there. Integrating AI into hospital security isn’t just about deploying new technology—it’s about rethinking existing systems. Many hospitals rely on outdated legacy systems, and merging these with sophisticated AI tools often requires costly and time-consuming modifications.
Sure, these concerns are real, but they’re not deal-breakers. With careful planning, open communication and rigorous testing, hospitals can overcome these hurdles and responsibly integrate AI to create safer, more efficient security systems.
Blueprint For AI Adoption In Hospital Security
Since most hospitals depend on third-party providers for security solutions, choosing the right AI-powered systems is critical to ensure they meet your needs and justify the investment.
Here’s how I recommend approaching this:
• Choose The Right Partner: Work with security vendors experienced in AI systems. Look for providers that offer scalable, compliant solutions with clear documentation of how their AI operates.
• Identify Security Gaps: Assess your hospital’s current vulnerabilities and prioritize areas where AI can deliver immediate value—such as visitor management, weapons detection or area surveillance.
• Start Small: Launch a pilot project, like automating entry-point security, before rolling out AI across the entire facility.
• Train Your Team: Equip your security staff with the knowledge and skills to operate AI systems effectively, empowering them to maximize the technology’s impact.
• Monitor And Improve: Continuously assess AI performance using real-time feedback. Use vendor support post-implementation to fine-tune the system as needed to ensure it delivers optimal functionality and maximum security.
Before It Gets Too Late
The clock is ticking as violence in healthcare escalates and mandates like California’s bill take shape. Meanwhile, 65% of U.S. hospitals are struggling to retain qualified staff, leaving critical safety gaps.
Early adoption not only helps address these challenges, but also positions your hospital as a leader in safety and innovation, building trust and credibility.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
