Healthcare organizations are cautiously evaluating AI-powered cybersecurity tools
There’s no question AI is a powerful tool, and its impact on cybersecurity, especially in healthcare, is already significant. The problem is precision. Healthcare leaders aren’t saying no to AI. They’re saying, “Prove it.” And they’re right to do so. When you’re dealing with patient data, regulatory risk, and real lives, untested solutions aren’t an option.
Over the last few years, cyber threats targeting healthcare have increased in both volume and complexity. The sector has historically been slow to invest in cybersecurity infrastructure, which makes it a prime target. At a virtual Healthcare Dive event in November 2025, this idea came through clearly. Heather Costa, Director of Technology Resilience at Mayo Clinic, said healthcare “has not been at the forefront” of cybersecurity innovation. That leaves a gap to fill, and AI vendors are lining up to close it.
But here’s where it gets interesting. Many tools already in the field claim to use AI. The term is overused. It’s on marketing decks, press releases, and pitch calls. That doesn’t mean all of them leverage real, autonomous AI to deliver better outcomes. Filtering out cosmetic AI from substantive solutions is now part of operational risk control. Sanjeev Sah, SVP of Enterprise Technology Services and CISO at Novant Health, noted that incidents can scale from thousands to millions in moments. That kind of volume simply can’t be analyzed manually. AI is needed, but it must deliver real triage, and fast.
Executives looking to invest aren’t just checking feature lists. They’re asking the right questions: Does this vendor understand our environment? Can it scale? Does it offer real-time data correlation and response capabilities? This isn’t just about buying smarter software. It’s about future-proofing your cybersecurity operations.
Understand the landscape and know that being cautious isn’t being slow. It’s about betting on tools that work, not just those that talk about it. If it doesn’t reduce your attack surface or improve your security event handling, it’s noise. And in healthcare, there’s no room for noise.
Organizations must rigorously evaluate the legitimacy and sustainability of AI vendors
The AI space is exploding with claims, startups, and sudden market entries. That’s no problem, innovation thrives on competition. But when you’re securing healthcare systems, due diligence isn’t a nice-to-have. It’s the baseline.
Right now, there are too many vendors pitching AI-powered cybersecurity tools, many without a clear product roadmap or long-term stability. William Scandrett, Chief Information Security Officer at Allina, flagged this directly. He pointed out that some AI companies are created quickly and lack economic viability; others are little more than marketing shells. His team evaluates a vendor’s financial health meticulously, checking 10-Ks and 10-Qs, and verifying if the company’s been around long enough to be trusted. That may sound basic, but it’s shocking how many offerings don’t pass the first filter.
Sustainability isn’t just about revenue growth. It’s about operational history. Has the vendor handled real incidents? Have they maintained security best practices through scale? These questions matter. Sanjeev Sah from Novant Health reinforced this point. His team considers a company’s incident response history, monitoring systems, and control mechanisms before any integration happens. If a vendor doesn’t meet their established requirements, they simply don’t move forward.
There’s also noise in the market around what constitutes actual AI. Some companies are repackaging old tools with new labels to ride the AI wave. This sort of rebranding slows progress. It can also mislead internal teams looking to improve security by adopting “AI” that, in practice, doesn’t add much beyond automation scripts or rule-based triggers.
C-suite executives need to establish clear standards that go beyond buzzwords. If the technology doesn’t satisfy due diligence from legal, compliance, and security teams together, it’s a liability. Procurement and security need to be in sync, asking deep questions and expecting complete answers.
Buying the wrong partner creates more than inefficiency. It increases exposure. The rule is simple: strong tech backed by a strong organization. No shortcuts.
AI offers significant advantages for healthcare cybersecurity
AI’s impact on cybersecurity isn’t theoretical anymore. It delivers operational speed, highlights real threats, and reduces time to response. But these benefits only materialize if the organization is ready for them. Leadership, governance, and internal collaboration must evolve at the same pace as the tools.
Healthcare systems operate with enormous amounts of sensitive data and narrow operational margins. That alone raises the stakes when deploying an AI-powered cybersecurity solution. Heather Costa, Director of Technology Resilience at Mayo Clinic, was clear about this at the Healthcare Dive event. She made the point that AI, while promising, is still an emerging technology. For it to create value, it demands the right people, aligned processes, and leadership that understands both cybersecurity and operational priorities. Without that alignment, even the best tools produce limited outcomes or worse, introduce new risk.
The operational side matters just as much as the technology. Leaders need to bring together cybersecurity, compliance, IT, and clinical operations before making a deployment decision. This isn’t about overlap, it’s about visibility. If AI systems are isolating data or generating decisions without clarity, you lose trust fast. And trust is what holds the entire system together.
Ethics must also be in focus. AI decisions impact patient data, real-time alerts, and system-wide responses. You don’t want models that behave unpredictably or that can’t explain how they arrived at a decision. That’s a governance failure, and it’s avoidable. Establishing ethical review processes from the beginning, not after things break, is how you prevent future complications.
Executives need to look beyond immediate functionality. A networked organization, where legal, security, and operations move together, has the best chance of deploying AI successfully. Cross-functional coordination isn’t bureaucracy. It’s discipline. AI in healthcare security can scale, but it does so best when it’s part of a system that’s built for accountability, not just speed.
Key highlights
- Evaluate AI tools with precision: Healthcare execs should scrutinize AI cybersecurity tools for real functionality, not just branding. The right tool must demonstrate the ability to reduce incident volume, prioritize threats, and integrate into existing ops.
- Vet vendor viability early: Leaders should assess financial stability, operational maturity, and true AI capabilities before partnering. Prioritize vendors with proven track records, strong monitoring practices, and clear incident response history.
- Align governance before AI deployment: Successful AI integration requires cross-functional leadership, ethical oversight, and coordinated processes. Building governance in early reduces risk and ensures the technology delivers value at scale.
