u/Saiprasanth-22

I've been running independent launch-readiness reviews on small products like extensions, web apps, tools like using a combination of static analysis, runtime testing, and source review. Not a penetration test. More like: "is this actually ready to ship?"

One product had 2,437 passing unit tests. Still had a layout overflow at side-panel width, partial search results showing as complete, and store disclosure copy that didn't match what the code actually did.

Another had privacy copy saying user data was "never shared with third parties." The code had Clerk, PostHog, Vercel Analytics, and Cloudflare RUM running. Not wrong to use those, wrong to say "never shared."

A third had two different builds both tagged v1.3.2. A fix verified in one build was being inherited by the other. The bug wasn't fixed where the user had it.

The pattern: builders trust their own test suites, their own copy, and their own reading of their product. An independent eye with evidence catches what familiarity hides.

I'm doing 10 free reviews before I charge for this. If you're shipping something in the next few weeks and want a real look, not vibes, not a checklist, evidence-backed findings with fix priorities, drop a comment or DM me.

What I'll cover: functionality, reliability, trust/privacy claims, UX, store listing, accessibility basics. What I won't pretend to cover: things I can't reach without your credentials.

Hi everyone. I’m a student working on a healthcare cybersecurity research prototype, and I’m trying to understand the real-world workflow before I make wrong assumptions.

This is not a product pitch, and I’m not asking for patient data, internal documents, network details, hospital names, or anything sensitive.

The problem I’m exploring is:

When hospitals have network-connected medical devices, cybersecurity decisions can’t always be “just block the traffic,” because the wrong action could interrupt clinical workflow or device visibility.

I’m trying to understand how this is handled in real hospitals.

Questions:

1. Who usually owns the inventory of network-connected medical devices?- IT?- biomedical engineering?- clinical engineering?- security team?- vendors?- nobody clearly?
2. If a device or device network has a cybersecurity issue, who gets involved first?
3. Are medical devices usually visible to the hospital SOC/security team, or mostly managed separately?
4. Does your organization track whether a security action could affect clinical workflow?
5. If a tool only ran in shadow mode and produced a risk/evidence report without blocking anything, would that be useful or just noise?
6. What would make a student-built tool in this space sound instantly unserious or unsafe?
7. Are there audit/accreditation/compliance processes where device inventory, incident logs, or continuity evidence matter?
8. What terminology would real hospital teams use for this problem? “Cyber-continuity” sounds clear to me, but I’m not sure if it sounds natural in hospital language.

Again, I’m not looking for confidential information. I’m trying to learn the ownership/workflow reality so I don’t build a fantasy system.

If you work in hospital IT, biomedical engineering, clinical engineering, or healthcare cybersecurity and are open to a few follow-up questions, I’d appreciate a DM. No product pitch, no data request.

Hi everyone. I’m a student working on a healthcare cybersecurity research prototype, and I’m trying to understand the real-world workflow before I make wrong assumptions.

This is not a product pitch, and I’m not asking for patient data, internal documents, network details, hospital names, or anything sensitive.

The problem I’m exploring is:

When hospitals have network-connected medical devices, cybersecurity decisions can’t always be “just block the traffic,” because the wrong action could interrupt clinical workflow or device visibility.

I’m trying to understand how this is handled in real hospitals.

Questions:

1. Who usually owns the inventory of network-connected medical devices?

   - IT?

   - biomedical engineering?

   - clinical engineering?

   - security team?

   - vendors?

   - nobody clearly?

2. If a device or device network has a cybersecurity issue, who gets involved first?

3. Are medical devices usually visible to the hospital SOC/security team, or mostly managed separately?

4. Does your organization track whether a security action could affect clinical workflow?

5. If a tool only ran in shadow mode and produced a risk/evidence report without blocking anything, would that be useful or just noise?

6. What would make a student-built tool in this space sound instantly unserious or unsafe?

7. Are there audit/accreditation/compliance processes where device inventory, incident logs, or continuity evidence matter?

8. What terminology would real hospital teams use for this problem? “Cyber-continuity” sounds clear to me, but I’m not sure if it sounds natural in hospital language.

Again, I’m not looking for confidential information. I’m trying to learn the ownership/workflow reality so I don’t build a fantasy system.

If you work in hospital IT, biomedical engineering, clinical engineering, or healthcare cybersecurity and are open to a few follow-up questions, I’d appreciate a DM. No product pitch, no data request.

I’m a student building a research prototype

The idea is not “another AI cybersecurity tool.” The core question is:

If a hospital medical-device/network workflow is under cyber uncertainty, how do you avoid two bad outcomes?

1. Fail-open: suspicious/corrupted traffic passes normally.

2. Fail-closed: security blocks too hard and clinical workflows stop.

The prototype is currently passive/shadow-mode only. It observes/simulates IoMT-style traffic, normalizes packets, runs detection layers, recommends continuity modes, and produces an evidence report.

The modes I’m exploring are things like:

- normal

- vitals-only

- local buffer

- reviewer hold

- quarantine nonclinical

- emergency bypass only when blocking may affect care

I’m especially thinking about messy hospital environments where infrastructure is not perfect, staff are overloaded, vendor access is messy, and manual fallback is common.

I’m not claiming this is deployable or safe for live clinical use. I’m trying to understand whether the problem framing is useful before I build further.

Questions:

- Is “cyber-continuity validation” a real gap in healthcare IT/security?

- Would passive/shadow assessment be useful before any active deployment?

- What would make this idea unsafe, useless, or already solved?

- If you work in healthcare IT/biomed/security, what would you immediately challenge?

I am building a network-layer anomaly detection system for hospital IoMT using CICFlowMeter-extracted features (CIC-IoMT-2024 dataset). Deployed as an inline tap inside the LAN, not a cloud monitor.

We just audited all 45 features against payload dependency:

• 37 are pure flow-based (timing, packet sizes, TCP flags, IAT, header metadata) - survive encryption completely
• 8 are app-layer protocol flags (HTP, HTTPS, SSH, DNS, etc.) - inferred via port matching in our dataset, degrade if devices use non-standard ports or tunneling

Question for people who've actually deployed security tooling in hospital environments: how common is non-standard port usage or tunneling in real IoMT deployments? Is this a marginal edge case or a real coverage gap we should solve before pitching this?

I am building a network-layer anomaly detection system for hospital IoMT using CICFlowMeter-extracted features (CIC-IoMT-2024 dataset). Deployed as an inline tap inside the LAN, not a cloud monitor.

We just audited all 45 features against payload dependency:

• 37 are pure flow-based (timing, packet sizes, TCP flags, IAT, header metadata) - survive encryption completely
• 8 are app-layer protocol flags (HTP, HTTPS, SSH, DNS, etc.) - inferred via port matching in our dataset, degrade if devices use non-standard ports or tunneling

Question for people who've actually deployed security tooling in hospital environments: how common is non-standard port usage or tunneling in real IoMT deployments? Is this a marginal edge case or a real coverage gap we should solve before pitching this?

Hi everyone, I’m a B.Tech AI and Data Science student building a prototype around cybersecurity for connected hospital medical devices like infusion pumps, patient monitors and ECG monitors.

The prototype looks at IoMT network behavior and tries to detect patterns such as reconnaissance, DDoS style volume attacks, integrity attacks and abnormal device behavior.

One design goal I care about is patient safety. If a device looks suspicious, I don’t want the security workflow to blindly block critical clinical data. The prototype separates clinical vitals forwarding from security routing, so safety data can keep moving while suspicious traffic is logged, restricted or escalated.

This is not a company, not a deployed product and not a sales pitch. I’m trying to understand whether this is solving a real problem or if I’m missing how hospitals actually handle this.

I’d really appreciate blunt feedback on a few things:

1. Do hospitals usually have enough visibility into medical device network traffic for this kind of detection to be useful?
2. Does separating vitals forwarding from security isolation make sense in a real hospital workflow?
3. What would make something like this useful instead of just another noisy alert tool?
4. Are there compliance, biomedical engineering or IT realities I’m probably missing?