In the IT world, we often talk about pain points. In healthcare cybersecurity, the primary burden is the cost of the noise.
- Hospitals are among the most targeted institutions in the world, and the industry response has been to sell more complex security stacks
- Every unnecessary alert in a clinical environment is a tax on patient care, not just on the security budget
- Thousands of hospital assets cannot support an endpoint agent, and tools that do not understand those devices generate false positives
- Analyst time lost to false positives is measurable, and so is the human capital lost to rule tuning
- Signal quality, not signal volume, is what determines return on a security investment
The Operational Mismatch
Hospitals are currently some of the most targeted institutions on the planet. The industry response has been to sell more complex security stacks. But a hospital isn’t a corporate office. It is a high-stakes clinical environment where every unnecessary alert is a tax on patient care.
Traditional tools rely on endpoint agents. But in a hospital, thousands of assets, including critical clinical systems, cannot support an agent. When tools don’t understand these devices, they produce false positives. For a CISO, these technical errors directly drain budget and human capital.
The ROI of Clarity
The Quantitative ROI for a 300-Bed Hospital
| ROI Vector | The Problem | Personam Impact | Monthly Value |
|---|---|---|---|
| Analyst Time Recovery | 10 to 15 minutes per false positive alert (IDC/Ponemon). Traditional tools generate thousands per month. | 3 to 5 high-confidence signals per month, 160 to 200 hours recovered | $13,600 to $17,000 at $85/hr fully burdened |
| Rule-Tuning Overhead | 8 to 12 hours per week maintaining legacy NDR/EDR rules in complex environments | Adaptive baselining eliminates manual rule writing entirely | $3,000 to $4,500 in human capital efficiency |
| Dwell Time Risk Reduction | IBM: breach cost is $1.2M lower for organizations containing in under 200 days | 70% reduction in investigative dwell time, threats in minutes not months | Actuarial risk reduction on average healthcare breach cost |
What Each Approach Sees
- Thousands of alerts per month, most of them noise
- Unagentable clinical devices reported as unknown or ignored
- Rules that require constant manual tuning to stay useful
- Analyst hours consumed triaging events that were never threats
- 3 to 5 high-confidence signals per month
- Every device profiled from its first network packet
- Baselines that adapt without manual rule writing
- Analyst attention directed at the small set that warrants it
The Personam Difference
Personam is built on a different premise: healthcare doesn’t need more alerts, it needs better ones.
Instead of chasing malware signatures, Personam uses behavioral AI to learn the behavior of the network itself. By baselining what is normal for a specific clinical workflow or medical device, deviations are detected in minutes, not months.
The winners against attackers will not be the tools that provide the most data, but the ones that provide the most clarity. Personam turns the chaos of hospital network traffic into actionable intelligence that protects both the bottom line and the bedside.
- IBM Security: Cost of a Data Breach Report 2024
- Ponemon Institute: The Economic Value of Prevention and Predictive Cybersecurity
- IDC: The Business Value of Modernized SOC Operations
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