AI Smart Security

Artificial intelligence is defined as having machines do “smart” or “intelligent” things on their own without human guidance. As such, AI security involves leveraging AI to identify and stop cyber threats with less human intervention than is typically expected or needed with traditional security approaches.
AI security tools are often used to identify “good” versus “bad” by comparing the behaviors of entities across an environment to those in a similar environment. This process enables the system to automatically learn about and flag changes. Often called unsupervised learning or “pattern of life” learning, this method results in large numbers of false positives and negatives. More advanced applications of AI security can go beyond simply identifying good or bad behavior by analyzing vast amounts of information and helping to piece together related activity that could indicate suspicious behavior. In this way, AI security behaves in a manner that’s similar to the best and most capable human analyst.

Microsoft and non-profit research organization MITRE have joined forces to accelerate the development of cybersecurity’s next chapter: to protect applications that are based on machine learning and are at risk of new adversarial threats.
The two organizations, in collaboration with academic institutions and other big tech players such as IBM and Nvidia, have released a new open-source tool called the Adversarial Machine Learning Threat Matrix. The framework is designed to organize and catalogue known techniques for attacks against machine-learning systems, to inform security analysts and provide them with strategies to detect, respond and remediate against threats.

As cyberattacks grow in volume and complexity, artificial intelligence (AI) is helping under-resourced security operations analysts stay ahead of threats. Curating threat intelligence from millions of research papers, blogs and news stories, AI provides instant insights to help you fight through the noise of thousands of daily alerts, drastically reducing response times.

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