Automated Patch Management for Endpoints: Ensuring Compliance in Healthcare and Education Sectors
Bibliographic record
Abstract
Automated patch management has become a critical factor in an organisation's security compliance plan and is perhaps more essential in industries such as the healthcare sector and education due to sensitive information.In the context of this article, we widen our understanding of patch management and describe an original, general approach to an automated patch management system as a solution for endpoint protection.Here, we highlight the issues with compliance with rules and regulations such as Healthcare in Information Technology, HIPAA (Health Insurance Portability and Accountability Act), and FERPA (Family Educational Rights and Privacy Act).It also evaluates the practices that are currently being used, their issues and why there is a need to automate the process to fix them.This new model also involves patch deployment procedures, ongoing compliance checks and endpoint health checks.This roughly entails the use of machine learning in deciding the prioritisation of patches and risk analysis.Employing practical datasets concerning both sectors, the effectiveness of the proposed approach was shown.We determined that we have accumulated 45% in assimilation of notable vulnerabilities within the least amount of time and decreased compliance violations by 32%.Finally, the issue of potential future work is discussed and includes the development of AI-enabled patch testing and the decentralised verification of compliance solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".