Delve into the world of AI in Edge Computing in this eye-opening article. Uncover the concealed risks and challenges that come with deploying AI at the edge.

As artificial intelligence (AI) and edge computing continue to reshape the digital landscape, organizations face new and evolving cybersecurity challenges. While AI offers promising solutions to enhance security measures, there are inherent risks associated with its implementation.

This is particularly concerning the need for more comprehensive testing playbooks. Existing attack simulation tools often focus on enterprise-specific scenarios, leaving gaps in testing methodologies for emerging areas such as edge computing and cloud infrastructure. In this article, we explore the limitations of current testing playbooks and the importance of developing AI-assisted tools that address these challenges head-on.

The Standard Playbook

The current state of attack simulation tools predominantly revolves around a standard playbook for enterprise networks. This playbook typically involves gaining access through phishing, exploiting vulnerabilities, or employing rogue devices. The subsequent steps involve unauthorized user account access, domain enumeration, privileged account identification, and ultimately obtaining unauthorized privileged access. The process often culminates in dumping domain hashes, marking the penetration test or simulation as complete.

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