Patients walk into a doctor’s office and are swiftly asked permission for artificial intelligence (AI) scribes to document notes full of sensitive health information. That intake conversation is just one entry point as AI is now embedded throughout the patient lifecycle, accessing more data than most organizations can secure.
This raises significant concerns for a sector that historically struggles to secure legacy equipment and highly sought-after data, even before AI was added to the mix. Advancements could save invaluable time during patient care, but security will present hurdles in the form of supply chain risks that stem from AI vendor compromise, data leakage through prompts, and AI-generated social engineering campaigns.
A recent Proofpoint report, “2026 Study on Cybersecurity in Healthcare” warned that “AI adoption is accelerating faster than governance.” Forty-five percent of organizations confirmed they had embedded AI into their systems but 60% lacked the necessary controls to govern that sensitive data.
Healthcare organizations are rapidly deploying AI to record intake conversations, and customer conversations increasingly revolve around how physicians are using AI for clinical decisions, and more importantly, how they are protecting those tools, explains Susan Biddle, director of healthcare solutions at Proofpoint. Some Proofpoint customers use the OpenEvidence open-source AI tool for research and diagnoses because it can integrate with Epic Systems, a widely used platform for Electronic Health Records (EHR).
“The challenge is making sure physicians aren’t putting sensitive information into that tool, so it does get challenging,” Biddle tells Dark Reading. Even seemingly generic details, like a patient’s eye color, combined with a specific injury like a left tibia fracture could qualify as Protected Health Information (PHI).
That makes data sensitivity a major concern when feeding information into AI tools. Therefore, organizations should focus on securing the underlying data rather than the tool itself, advises Biddle.
What Do CISOs Want to Know about AI in Healthcare?
AI’s use is spreading fast and that complicates how organizations think about risk. Tony Sabaj, head of Americas channel SEs at Check Point, says AI’s pattern-recognition capability is already improving clinical care, catching early signs in cancer screenings, triaging critical patients, and reviewing electrocardiograms to “generate reports faster and with more accuracy.”
Beyond the clinic, health insurers are using AI for claims processing and fraud detection, and like other industries, healthcare is building digital twins to model operational scenarios at scale.
Some of AI’s most visible momentum is in reducing administrative burdens including documentation that converts clinician-patient conversations into draft notes, scheduling, intake, coding, and patient communications, says Cindi Carter, global chief information security officer (CISO) at Check Point. But she warned it’s also being used behind the scenes to support operational planning, research, and cybersecurity.
Organizations often sort AI tools into “medical” or “administrative” buckets. The first consists of diagnostic AI or treatment recommendation systems, while the latter includes intake forms or scheduling. Carter is concerned that these labels may undermine the downstream risks administrative tools can still pose because a “documentation tool can still influence what becomes part of the medical record” and lead to clinical consequences.
In pharmaceutical and precision medicine, AI investment is outpacing security investment, Sabaj says. And as AI reaches further into healthcare, organizations need to ensure existing regulatory frameworks keep pace. Guardrails often center on protecting data leakage, but safeguards should also ensure that if AI is taking any kind of medical action, it’s limited to known parameters, he recommends.
“We continue to see breaches of medical information and AI has the potential to exacerbate this issue,” Sabaj tells Dark Reading. To get ahead of the threat, some hospitals are building their own AI tools, using open weight models and training large language models on proprietary data rather than relying on public platforms to handle sensitive PHI.
Carter’s concerns extend beyond confidentiality. Despite progress, AI is known to hallucinate. What if AI produced an inaccurate summary, omits an important detail, or acts on manipulated information? The organization can lose confidence in the information supporting care without a record ever being stolen, she adds.
The CISO question then becomes broader, shifting from “What data left?” to “What can this system see, what can it change, and can we reconstruct what happened?” she says.
“In healthcare, we are protecting sensitive information and the integrity of decisions that depend on it,” she explains.
Leading Causes of Major Healthcare Breaches
In the relatively short time span since rapid AI adoption took off, Phil Englert, VP of medical device security at Health-ISAC, has already observed changes that will affect how much sensitive data floods systems. As AI becomes increasingly integrated, when a cyber incident occurs, organizations frequently lose patient identifiers, medical records, insurance details, and other clinical or financial data— all considered PHI.
Englert cited recent Health and Human Services data that shows hacking and other IT-related incidents continue to be the leading causes of major healthcare breaches. Denver Health Medical Plan reported a breach Oct. 1 that affected 238,083 individuals, triggered by an exposed vendor access key through AI platform SquareML.
This tracks with Health-ISAC’s 2026 threat landscape findings which found that many significant breaches have originated through third-party vendors, “reinforcing the idea that an organization’s security posture is often only as strong as that of its partners.”
While organizations need to secure AI within their own environments, they also need to protect themselves against it as threat actors use it to craft more convincing and effective social engineering attacks, warns Englert. Healthcare organizations are seeing increasingly sophisticated phishing campaigns, deepfakes, voice impersonation attacks, and QR-code phishing— any method to exploit human trust in a sector driven by speed and precision.
While healthcare organizations can’t seem to shake the ransomware threat, which “remains one of the most damaging facing the sector,” AI-enabled attacks are a top cybersecurity concern for 2026, he adds.
“Cyber Safety is Patient Safety”
Implementing effective guardrails has proved significantly challenging. Carter offers five recommendations informed by the governance and lifecycle risk themes in the Joint Commissions and the Coalition for AI Health guidance: Visibility, boundaries around data and actions, evidence from vendors, validation and meaningful human oversight, and continuous monitoring.
Organizations should maintain AI inventory and give staff a straightforward way to disclose unsanctioned uses as shadow AI risks pummel the sector. Limiting what data AI can handle e is also essential; Carter recommends that access to information should not automatically confer authority to update records, send communications, or initiate transactions.
“AI compresses the time between a mistake and material loss,” Carter warns. That should be reflected in organizations’ safeguards.
Implementation also depends on organizational maturity. While talking with security officers across the industry, Proofpoint’s Biddle observed varying levels of AI adoption. Some are quite far in the process, while others are still cautious and taking it slow, she adds. She points out healthcare does tend to lag behind other industries when it comes to technology.
Many organizations lack resources but continually face disruptive attacks that severely affect patient care. “It doesn’t seem to be getting any better, unfortunately, “Biddle says .
Getting better requires money, but when CISOs stand in front of the board and request investments to shore up defenses, they are often hit with: “Why do you need to spend this amount?” says Biddle. It’s difficult, because they are competing with budget dollars for life-saving devices, she says.
“But I would say: Leverage the impact of cyber safety,” Biddle adds. “Cyber safety is patient safety.”
