Two new tools are helping cybersecurity professionals fight the vast volume of threats and attacks— artificial intelligence (AI) and machine learning. This important subject came to us from Tulane University in their article, “Is artificial intelligence, machine learning the answer to defending against cybersecurity attacks?“
Even with the advancements in technology, AI and machine learning security techniques are still in their infancy. However, they have proven themselves to be valuable assets in assisting analysts in finding vulnerabilities in data sets too large to cover effectively manually.
Unfortunately, criminals are using some of the same advanced tools.
The solution could lie in defensive AI or self-learning algorithms that understand normal user, device, and system patterns in an organization and detect unusual activity without relying on historical data. But the road to widespread adoption could be long and complicated as cybercriminals look to stay one step ahead of their targets.
Most organizations have little knowledge on how AI systems make the decisions they do. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
