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MedTech companies mistakenly assign product cybersecurity to their IT teams, whose focus is data protection. Product security is about patient safety and should be owned by Quality Assurance, as all documentation must integrate into the Quality Management System (QMS) like other design files.
Unlike a biocompatibility study that can be scheduled for a specific quarter, cybersecurity cannot be treated as a one-time milestone. It must be an iterative process integrated throughout the entire product lifecycle, from initial design and software development to post-market surveillance.
Pursuing 100% security is an impractical and undesirable goal. Formal methods aim to dramatically raise assurance by closing glaring vulnerabilities, akin to locking doors on a house that's currently wide open. The goal is achieving an appropriate level of security, not an impossible absolute guarantee.
Organizations that default to treating AI as an IT-led initiative risk failure. IT's focus is typically on security and risk mitigation, not growth and innovation. AI strategy must be owned by business leaders who can align its potential with customer needs, talent decisions, and overall company growth.
Instead of creating a massive risk register, identify the core assumptions your product relies on. Prioritize testing the one that, if proven wrong, would cause your product to fail the fastest. This focuses effort on existential threats over minor issues.
Retrofitting cybersecurity into a medical device near submission is a common, catastrophic error. The FDA requires security to be designed-in from the start. "Bolting it on" later leads to significant delays and costs, much like trying to add rebar to an already-poured foundation.
At a massive scale like Twitter's, even innocuous features can be weaponized in unforeseen ways. A formal Product Requirements Document (PRD) process, including reviews with teams like Trust & Safety, is vital for identifying and mitigating potential misuse before development begins.
The primary lens for medical device cybersecurity should be patient safety, not data protection. A hacked device can directly harm a patient, making security as fundamental as sterility. This reframing changes the entire approach from a compliance checklist to a core design principle.
In high-stakes regulated fields, documentation like FMEAs is not red tape. It's a critical tool for understanding failure modes, mitigating risks, and ensuring product viability and patient safety, especially for a startup where one recall can be fatal.
Industrial control systems (OT) on factory floors are largely unencrypted and unsecured, a stark contrast to heavily protected IT systems. This makes manufacturing a critical vulnerability; an adversary can defeat a weapon system not on the battlefield, but by compromising the industrial base that produces it.
While AI cybersecurity is a concern, many MedTech innovators overlook a more fundamental danger: the AI model itself being flawed. An AI making a wrong recommendation, like a therapy app encouraging suicide, can have dire consequences without any malicious external actor involved.