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AI cybersecurity is a critical part of any organization’s security strategy, bringing data analysis and threat identification to help minimize breach risk and enhance security posture. It also provides the much-needed context for prioritization and response to security alerts, for fast incident response, and for surfacing root causes in order to mitigate vulnerabilities and avoid future issues.

Cybersecurity is a rapidly evolving industry, and cybersecurity experts are constantly finding new tools that can help to protect organizations from attacks. But as these technologies advance, it’s important to keep in mind that they can be abused by bad actors as well.

Machine Learning (ML) algorithms are vulnerable to attacks that alter their functionality by manipulating the data they receive. This can lead to false positives, which could have devastating consequences for a business.

As such, cybersecurity professionals need to use AI in the right ways and with the proper resources. For example, ML models are not self-sufficient and should be used with human cybersecurity specialists who can review results and provide recommendations.

In addition, the use of ML models should not be interpreted as “replacing a team” but rather, complementing the cybersecurity team to increase productivity and efficiency. This means explaining recommendations and analysis to stakeholders across the enterprise and reporting relevant information to a variety of audiences, including end users, infosec operations, CISOs, auditors, CIOs and CEOs.

It’s a growing market for products that leverage AI to help prevent, detect and respond to cybersecurity threats. A July 2022 report by Acumen Research and Consulting says the global market for AI-based cybersecurity products was $14.9 billion in 2021 and is expected to reach $133.8 billion by 2030.