AI and ML based Threats for Cybersecurity
manufacturingtechnologyinsights

AI and ML based Threats for Cybersecurity

By Manufacturing Technology Insights | Thursday, May 23, 2019

CybersecurityFREMONT, CA: Cognitive technologies have gained a strong foothold in the digital space with innovative features. These features allow organizations to increase the efficiency of their business processes and applications. Technologies like artificial intelligence (AI) and machine learning (ML) analyze the operations of a company to improve its services and increase productivity. These technologies have also become a crucial aspect of an organization’s cybersecurity strategies. However, many cybercriminals have started leveraging these technology tools to hack into a company's system and rob them of their treasured data.

Although the use of AI and ML in cyber attacks is still in its infancy, hackers are gradually increasing their attack intensity with the help of these technologies. The cybercriminals are using the same logic and functionality of AI and ML to find loopholes in a company’s defenses. The ML tools can allow hackers to speed up the attack process with more efficiency, creating significant risks for the cybersecurity teams. These tools are even capable of breaching into the cyber defenses which were designed with the help of AI and ML technologies.

Deep learning technology can offer some respite for the security teams in an enterprise as they can use various feature inputs to detect anomalous behavior in an organization’s network. It is extremely significant for security teams to detect if their system has been injected with bad data or the ML models have been poisoned to deter them from the attack vectors. The poisoning of ML models will allow cybercriminals to create a baseline behavioral paradigm that enables their malicious activities to attain a low-risk score. Companies need to operationalize their ML models in such a way that it can reduce false positives and false negatives. AI and ML tools should not only be able to detect patterns, but they need to justify recommendations about how to deal with them by providing context for the decision-making.

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