As new technologies gain traction, many organizations realize the weaknesses around standardized authentication, static networking and trust-based security models. This interesting and important topic came to us from Data Quest in their article, “5 ways to use emerging technologies to enhance traditional approaches to cybersecurity.”
Because of emerging technologies, the digital footprint of organizations has expanded like never before and this led to increased cybersecurity risks and attacks. In order to mitigate cyber risks and improve organizations’ security posture, companies need to adopt a risk-driven cybersecurity program and optimize their cybersecurity investments.
One of the primary catalyst for the increased cyber-threat is the transition to remote and hybrid working. Security policies have been rewritten, best practices reshaped and new challenges burgeoned. However, among the most promising technologies in cybersecurity is undoubtedly artificial intelligence (AI) and particularly machine learning.
The challenge lies in understanding. Most organizations have little knowledge on how AI systems make the decisions they do, and as a result, how the results are being applied in the various fields that AI and machine learning are being applied. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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