Delve into the world of edge computing and AI 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.
However, organizations with DevOps-led environments often lack traditional domains, requiring different attack simulations. For instance, there is a growing need for attack simulations specific to cloud infrastructure platforms like Azure and AWS. These simulations should focus on exploiting privileged user accounts, manipulating infrastructure components, or compromising work nodes in a cluster. While some limited forms of such simulations exist, comprehensive solutions are still lacking.
One of the major hurdles in adopting attack simulation tools, particularly those assisted by AI, is understanding an organization’s tolerance for complexity. Today’s compliance-led testing landscape often demands stability and resiliency, making it challenging to perform tests that could potentially disrupt services or render systems unstable. Balancing the need for thorough testing with the risk of unintended consequences becomes crucial.
Expanding the Playbook
To highlight the need for evolving testing playbooks, consider a new attack method that has emerged – the “path to domain admin” playbook. This method involves gaining access to the network, enumerating VMware ESXi and Vcenter systems, exploiting unpatched vulnerabilities, and stealing the service account used to domain enable authentication for VMware service to gain domain admin rights. While this method has proven effective, its implementation may pose stability concerns, particularly for hypervisors during the exploit phase.
The current role of AI in attack simulations primarily revolves around scaling formulaic testing playbooks. However, there is a pressing need to develop AI-assisted tools that address real-world scenarios, manual testing processes, and complex security engagements. These tools should provide enhanced scalability, automation, and precision, while accounting for the organization’s tolerance for disruption and system instability.
AI and edge computing offer tremendous potential for innovation and efficiency, but they also introduce new risks to cybersecurity. Recently the Biden-Harris administration announced that the National Institute of Standards and Technology (NIST) is launching a new public working group on artificial intelligence (AI) that will build on the success of the NIST AI Risk Management Framework. Given the unprecedented speed, scale, and potential impact of generative AI, this move underscores the need to identify and develop tools to better understand and manage those risks.
To ensure comprehensive security, organizations must embrace the development of advanced attack simulation tools that address the specific challenges of emerging technologies and complex environments. By striking a balance between AI assistance, thorough testing playbooks, and organizational tolerance for disruption, we can mitigate the risks associated with AI and edge computing while reaping the benefits of these transformative technologies.
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