By Zac Amos, Features Editor, ReHack
LinkedIn:Â Zachary Amos
LinkedIn:Â ReHack Magazine
Healthcare systems depend on data exchange among electronic health records, connected medical devices and administrative networks. This creates environments where authorized clinical activity can closely resemble unauthorized access. Signature-based security tools may miss unauthorized activity that does not match a known attack pattern. AI-powered behavioral cybersecurity addresses this challenge by first learning normal patterns, then flagging deviations that warrant closer examination.
What Behavioral Cybersecurity Means for Healthcare Organizations
Behavioral cybersecurity analyzes activity across users, devices and systems to establish a baseline of normal behavior, then identifies patterns that fall outside expected parameters. Traditional signature-based tools detect known malicious patterns by matching against databases of previously identified attacks. Behavioral analysis takes a different approach by flagging suspicious activity that does not match an existing signature.
An employee account accessing an unusually high volume of patient records or a medical device communicating with an unfamiliar external destination could indicate a security incident. These deviations do not automatically signal malicious activity, so security teams must evaluate them within the context of legitimate clinical workflows before determining whether further action is necessary.
Common Attacks Facing Healthcare’s Digital Infrastructure
Healthcare organizations hold sensitive patient information and depend on connected systems to deliver care, making security incidents a concern for both data privacy and patient safety. The U.S. Department of Health and Human Services received breach reports affecting approximately 242.9 million individuals in 2024, with hacking and IT incidents accounting for 81% of large breaches. The healthcare sector faces several categories of attacks, including:
- Ransomware and other malware: These attacks can encrypt or steal patient information and turn off systems essential to clinical care, creating disruptions that compromise patient safety.
- Phishing: Adversaries use deceptive messages to trick employees into revealing credentials or opening malicious files, granting unauthorized network access.
- Malware-as-a-service: This model provides threat actors with access to ready-made malicious software and supporting services, lowering the barriers to launching attacks.
- Supply chain attacks: Adversaries compromise healthcare organizations through trusted software vendors or service providers rather than targeting networks directly.
HHS HC3 tracked 730 attacks against the healthcare sector worldwide in a six-month window, including more than 530 affecting the U.S. healthcare and public health sector. Nearly half of the U.S. incidents were ransomware-related. The report shows ransomware groups use multiple initial access methods, including phishing, credential compromise, remote access exploitation and vulnerability exploitation.
How AI Strengthens Behavioral Detection Across Healthcare Environments
AI can analyze electronic health record access patterns, connected medical device activity and staff behavior across clinical and administrative systems. Machine learning can build and update behavioral baselines, helping security teams spot unusual record access or device communications. Reliable data and clinical context also help teams separate suspicious behavior from legitimate changes in care workflows.
Baselines can become more useful when AI helps prioritize alerts based on risk, allowing security teams to focus on activity that warrants closer investigation. Behavioral analytics can weigh unusual activity against an account’s typical access patterns and permissions, helping analysts decide what needs attention first. A late-shift login may warrant a different response from a service account suddenly querying thousands of patient records.
Encryption can complicate inspection, since protecting patient data in transit may limit what security tools can examine. Some AI-based tools analyze communication patterns and connection metadata to identify suspicious behavior without decrypting the underlying content. This can support detection while preserving encryption protections.
Behavioral detection can also help identify suspicious activity that does not match a known attack signature. For example, an account accessing unfamiliar systems and transferring an unusual volume of data may warrant investigation even when no known malware is detected. These signals give security teams additional context to investigate potential incidents before they disrupt care.
Governance and Best Practices for Adopting AI-Driven Security
Healthcare IT teams can begin by identifying where AI can strengthen existing security tools and address visibility gaps in monitoring. Security analysts should review AI-flagged activity and retain control over response decisions, especially when actions could interrupt patient care. Clear governance should define access to sensitive data, accountability for decisions and the evidence needed to explain alerts.
NIST’s AI Risk Management Framework calls for policies that define roles and responsibilities for human oversight of AI systems. In healthcare, this means establishing who reviews flagged activity and who can authorize a response. AI can assist with triage and repetitive workflows while security teams remain responsible for investigation and decision-making.
Building a Resilient, Behavior-First Security Posture
AI-powered behavioral analysis can complement signature-based defenses by identifying suspicious activity within healthcare’s everyday digital workflows. Combining behavioral detection with existing security tools and human oversight can help healthcare organizations protect patient information and maintain continuity of care.