Threat modeling is a structured approach to identifying potential security threats, vulnerabilities, and attack vectors within a system. As defined in the Threat modeling – A systematic literature review by Xiong Wenjun and Robert Lagerström, "threat modeling is a process that can be used to analyze potential attacks or threats, and can also be supported by threat libraries or attack taxonomies." In healthcare applications, this process involves analyzing how patient data flows through systems, where it's stored, who has access to it, and what could go wrong at each stage. Traditional threat modeling has been a manual, time-intensive process requiring security experts to map out system architectures and brainstorm potential attack scenarios. According to the systematic literature review, "most threat modeling work remains to be done manually, which can be time-consuming and error-prone."
Automated threat modeling leverages specialized software tools and artificial intelligence to streamline this process. These systems can analyze application code, infrastructure configurations, data flow diagrams, and system architectures to automatically identify potential security risks.
Learn more: How healthcare workers can identify fake cracked apps before downloading
Healthcare data is valuable on the black market. According to "Cybersecurity in healthcare: A systematic review of modern threats and trends," medical information is "20 to 50 times more valuable to cybercriminals than personal financial information" because it contains personal data that can be used for identity theft, insurance fraud, and other malicious purposes. More recent analysis reinforces this, according to Kroll's research cited by the American Hospital Association (AHA), a healthcare record can be worth as much as $1,000 on the black market. The AHA reported that by the end of 2024, 259 million Americans' healthcare records had been stolen in part or full, and since 2020, over 500 million individuals, more than the entire US population, have had their healthcare records stolen or compromised at least once. Ransomware attacks on hospitals can put lives at risk when critical systems become inaccessible.
The complexity of modern healthcare IT environments makes manual security assessments impractical. A hospital system might include electronic health record platforms, picture archiving systems for medical imaging, laboratory information systems, pharmacy management software, billing systems, patient portals, mobile health applications, and countless connected medical devices. As noted in the systematic review, "medical devices, which were traditionally stand-alone systems, are becoming network-integrated within hospital IT systems and are no longer immune to traditional cyber attacks." Each system represents a potential entry point for attackers, and each integration point between systems creates additional risk.
"Cybersecurity in healthcare: A systematic review of modern threats and trends" found that "organizations hoping to comply with federal initiatives are spending around 95 percent of their IT budgets on implementation and adoption, while less than 5 percent of their IT budgets are spent on security." This imbalance leaves healthcare organizations vulnerable as they race to modernize without adequate security investment.
Automated threat modeling helps healthcare organizations keep pace with this complexity by continuously analyzing the security landscape. Rather than conducting annual or quarterly security reviews that quickly become outdated, automated tools can provide ongoing threat intelligence that adapts as applications are updated and new vulnerabilities emerge.
Modern automated threat modeling tools use several techniques to identify security risks. Static code analysis examines application source code without executing it, looking for common vulnerability patterns like SQL injection points, insecure authentication mechanisms, or improper data validation. Dynamic analysis tests running applications by simulating attacks and observing how systems respond.
Many advanced tools use machine learning algorithms trained on vast databases of known vulnerabilities and attack patterns. These systems can recognize security anti-patterns even in custom healthcare applications, flagging code segments that resemble previously exploited vulnerabilities. As John Riggi, National Advisor for Cybersecurity and Risk at the AHA, notes, "We're in the early stages of an artificial intelligence-fueled arms race, with the bad guys using AI to launch cyberattacks and the good guys using it to defend against those cyberattacks." Some platforms integrate directly with development environments, providing real-time feedback to programmers as they write code, catching security issues before they're ever committed to production systems.
The primary advantage of automated threat modeling is speed. What might take security teams weeks to accomplish manually can be completed in hours or even minutes with automation. This acceleration is crucial in healthcare, where deployment of new features or emergency security patches can save lives. The systematic literature review emphasizes this point, noting that "the research trend is to model a system with a higher degree of automation, e.g., automate the security analysis and/or the modeling."
Consistency is another benefit. Human analysts, no matter how skilled, bring varying perspectives and might miss threats depending on their experience or what they prioritize. Automated tools apply the same analysis to every component, ensuring comprehensive coverage. They don't suffer from fatigue or oversight, maintaining vigilance across thousands of potential attack vectors.
Not every healthcare organization can afford dedicated security architects for every development team. The systematic literature review notes that threat modeling "provides a structured way to secure software design, which involves understanding an adversary's goal in attacking a system based on system's assets of interest." Automation embeds expert knowledge into tools that development teams can use independently, raising the overall security baseline across the organization. Junior developers gain access to the same threat detection capabilities that would normally require years of security experience.
From a compliance perspective, automated threat modeling provides auditable documentation of security analysis. Healthcare organizations must comply with regulations like HIPAA in the United States, GDPR in Europe, and various other regional data protection laws. Automated tools generate detailed reports showing what security considerations were evaluated, what risks were identified, and what mitigations were implemented.
False positives remain a challenge. Automated tools can flag potential vulnerabilities that aren't actually exploitable in the specific context of a healthcare application, leading to alert fatigue. Security teams must develop expertise in triaging automated findings, distinguishing genuine risks from theoretical vulnerabilities that don't apply in their environment. The systematic literature review acknowledges this concern, finding that "there is limited assurance of their validations" across threat modeling methods in general.
Furthermore, research shows that "most security breaches are caused by employees accessing malicious files and most HIT security systems will not stop those kinds of breaches," as documented in the healthcare cybersecurity review. While automated threat modeling excels at identifying technical vulnerabilities, it must be complemented by security awareness training and organizational security culture.
Integration with existing workflows requires careful planning. The most effective automated threat modeling happens when tools are embedded into CI/CD pipelines, automatically analyzing every code change before deployment. However, achieving this integration in legacy healthcare IT environments can be technically challenging and may require infrastructure modernization.
The question healthcare organizations must ask themselves is not whether they will face a cyberattack, but as the AHA frames it, "When we are attacked, will we be ready?" Automated threat modeling provides a foundation for preparedness, enabling organizations to identify and address vulnerabilities before adversaries can exploit them. With patient safety, data privacy, and organizational survival all on the line, automation transforms threat modeling from an occasional security exercise into a continuous, embedded practice that keeps pace with changes.
Read also: Modernization of healthcare legacy systems
Automated threat modeling continuously analyzes systems, while traditional assessments are periodic and quickly become outdated.
No, automation supports but does not replace human judgment, especially for evaluating context-specific risks.
It can, but successful integration often requires updates to older infrastructure.
They should run continuously or automatically during every code change in the development pipeline.
Yes, they can provide expert-level insights without the need for large security teams.