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How Machine Learning, AI & Deep Learning Improve CybersecurityMachine intelligence is influencing all aspects of cybersecurity. Organizations are implementing AI-based security to analyze event data using ML models that identify attack patterns and increase automation. Before security teams can take advantage of AI and ML tools, they need to know what is possible. This report covers:
-How to assess the vendor's AI/ML claims
-Defining success criteria for AI/ML implementations
-Challenges when implementing AI
Incident Readiness and Building Response Playbook
In this special report, learn the Xs and Os of any good security incident readiness and response playbook.
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1 Comments
User Rank: Apprentice
4/13/2016 | 12:57:29 PM
Who is liable when the patient's health is further compromised because they did not receive the proper procedure/care at the right time?
Operating premise is that the healthcare entity pays the ransom, and gets the data back. If the data modified and the patients health is compromised as a result of incorract/inaccurate information in their E.H.R, whos assume liability? Who validates the integrity of the data following a recovery from the ransomware attack?
Most hospitals have standby procedures which they fall back on during a major IT outage. Older physicians came of age in a pen & paper world and can generally switch back to paper based notes, orders, and patient records. But younger Docs are generally clueless in this regard. They have always worked in the E.H.R and digital environment -they never had the pen & paper experience. So thier success is dependent on regular training and drills.
Some hospitals keep their standby procedures on the computer/network. This makes for tough sledding when your off the network or down to minimal electrical power.