The rise of artificial intelligence (AI) is a double-edged sword for healthcare. Many claim it can revolutionize care and enhance security systems, while others consider it the biggest financial and operational threat the industry faces in 2025.
This can be disorientating for healthcare leaders: Will AI protect their IT systems or infiltrate them?
Our experts argue both are plausible – and the results will come down to how you approach cybersecurity in the coming years. This article offers their expert analysis of the threat landscape and provides both “pros” and “cons” to the ever-faster emergence of AI in healthcare.
While we explore the intersection of AI and cybersecurity in healthcare, expect to learn:
- How generative AI could lead to a significant uptick in cybercriminal activity
- Why “assessment fatigue” will soon be a thing of the past
- What leading healthcare organizations should do this year to make AI their best security weapon
The “Cons” of AI for Healthcare: How AI Could Compromise Cybersecurity
AI has already been proven to deliver extraordinary benefits within healthcare – from enhancing cancer diagnoses to developing personalized treatment plans. However, there are also a few potential challenges healthcare organizations must consider:
1. AI Vulnerabilities
The healthcare industry has embraced AI very quickly. While just 18.7% of hospitals had adopted some form of AI in 2022, reports from the end of 2024 found that 86% have now started introducing AI-based solutions. But that speed can come at a cost.
Many healthcare organizations already struggle to meet cybersecurity best practices; the speed at which AI is being adopted may outpace security teams’ capacity to adapt to it. AI solutions ultimately expand the already-large attack surface at most healthcare organizations – and therefore have the potential to produce a few vulnerabilities:
- Adversarial Attacks: AI systems can be susceptible to “adversarial attacks,” where inputs are intentionally manipulated to cause the AI to make errors. This could take the form of misclassifying medical images, leading to potential misdiagnoses and delayed treatment; it could lead to EHR analysis that leads to the wrong treatment – and have disastrous consequences for patient health.
- Security Blind Spots: Third-party vendor networks are a well-known weakness of healthcare IT security, with the sheer scale and complexity of software used in the average organization creating an unmanageable workload for most security teams. Introducing new vendors can exacerbate existing weaknesses in TPRM and lead to a greater risk of a breach.
2. Malicious Actors
In the wrong hands, AI can be leveraged for nefarious means. While it is unclear how often this will occur, it’s important to note the potential for AI to equip cybercriminals in two ways:
- Scalability: The “leg work” required to deploy an attack will be reduced by AI, while outputs could be significantly improved. For example, generative AI can be used to create more plausible-looking phishing emails or deepfakes that will enable infiltration. Given that 11% of healthcare employees are given zero training to identify and mitigate phishing attacks, a sudden increase in their prominence and effectiveness could prove a serious problem.
- Vulnerability Analysis: AI can also significantly empower criminals to identify weaknesses within healthcare cybersecurity systems. There is already a “cat and mouse” game between attackers and security teams; the introduction of AI will accelerate the need to adapt defenses against new and more sophisticated attacks.
But while this paints a negative picture of the outlook, there are many reasons to believe AI will be a net positive for healthcare security leaders.
The “Pros” of AI for Healthcare: How AI Will Support Security Leaders in 2025
Our experts point to three promising use cases for AI in cybersecurity:
1. Automated Assessment Support
As mentioned above, adding AI tools could contribute to the total risk in healthcare organizations’ vendor networks. But AI can also help dramatically improve the efficiency and effectiveness of TPRM programs – more than making up for the increase in total vendor volume.
A simple example is vendor assessments: existing processes require a large amount of manual effort to create, disseminate, and analyze valid assessments for every vendor. Some companies struggle to scale their program and miss key vendors, while others reuse outdated questionnaires that make vendors take your efforts to audit their security less seriously.
Generative AI can streamline all of this and automate much of the hard labor involved in TPRM assessments. With the right tools and processes in place, it will be far easier and more sustainable to assess all vendors and quickly identify which pose the largest security risks.
Better still, this applies to all assessments – from third-party vendors to your annual HIPAA SRAs. Our experts use generative AI to deliver virtual assistants that guide even relatively inexperienced assessors through cohesive and comprehensive assessments; this could be a big win for smaller organizations that lack in-house cybersecurity resources or compliance expertise.
2. Streamlined HITRUST Certification
HITRUST certification is the gold standard for healthcare cybersecurity, with 99.4% of certified organizations avoiding a single security breach over the last two years. The problem is that HITRUST has very high standards, and the certification program is long and complex – leading many organizations to believe it is unattainable.
Generative AI tools will reduce the barriers to entry and eliminate much of the off-putting work involved in certification. Automated compliance documentation and support during the assessment and remediation phases of the program will give even relatively small security teams the opportunity to radically shift their security posture.
3. Modernize Integrated Risk Management
Integrated risk management (IRM) is a comprehensive approach to cybersecurity that unifies all areas of risk under a single function. It has grown in popularity within healthcare over the last few years, but many of its promises have remained tough to fulfill:
- Unifying risk data is difficult for organizations with large, fragmented IT system
- Predicting risk has proven tough without a clear view of the entire organization’s attack surface
- Limited resources have made adopting new tools to support IRM difficult
AI will change all of this – and could make IRM the industry standard within a few years. As IRM platforms integrate AI capabilities, the business case for adopting them will become hard to refuse – especially given the increasing risk landscape.
From tracking assessments across the entire organization to forecasting emerging vulnerabilities, security leaders will be equipped with real-time data analysis that can inform more effective decision-making – and ultimately keep their patients, reputation, and bottom line safer.
We know this because our own IRM platform is already doing these things – and the results have been striking. BluePrint Protect™ enables healthcare security teams to centralize risk data from their entire organization, develop a cohesive risk register, and leverage cutting-edge AI capabilities to unlock a truly proactive security approach.
AI and Cybersecurity in Healthcare: The Conclusion
We believe this demonstrates why AI will be a net benefit for healthcare cybersecurity. Cybersecurity teams have always been shackled by resource limitations, budget constraints, and visibility problems. Eliminating these barriers will unlock their true potential – and BluePrint Protect™ is using AI to do exactly that.
Want to explore how it could help your organization adapt to evolving risk?
