Hybrid and multi-cloud management serve as the bedrock of modern business operations, leading the charge in harnessing the full potential of cloud technologies. Yet, according to a commissioned study conducted in 2023 by Forrester Consulting on behalf of Tenable, the vast majority of Australian cybersecurity and IT leaders (68%) view cloud infrastructure as the greatest source of cyber risk in their organisation. The complexity introduced when trying to correlate user and system identities, access, and entitlement data was a primary concern cited in the study.
While cloud ecosystems offer scalability and flexibility, they also introduce inherent security challenges if the contextual view of cloud assets, digital identities and permissions is limited. When cyber teams examine the risk to their infrastructure and data, often the biggest challenge is deciphering the immediate course of action.
A Measured Approach
Tackling these challenges demands a proactive strategy, where generative AI assumes a central role in scrutinising vast arrays of assets, vulnerabilities, threats, and other datasets to swiftly pinpoint organisational risks and offer context that would otherwise be challenging to consider.
To illustrate, imagine the disparity between merely reviewing an asset inventory and identifying 100 laptops in the system, all harbouring vulnerabilities necessitating patching. While incorporating additional data and context aids teams in prioritising patch deployment, manually integrating this information is time-intensive and leaves the organisation vulnerable to attacks during the decision-making process.
Distilling Insights
Generative AI tools can efficiently ingest diverse datasets concerning assets, swiftly furnishing vital context to analysts. This elevates the analysis from a focus on a group of 100 laptops to pinpointing the two laptops belonging to the CEO and CFO, possessing administrative access, housing critical business data, and being authorised to access crucial backend databases of customer information. By delivering such granular insights directly to security professionals, they can promptly discern where to concentrate efforts and prioritise patching these critical systems, thereby mitigating substantial overall business risk.
However, for most organisations to harness the full potential of generative AI, they must first prioritise fundamental cloud security principles. These include ensuring comprehensive visibility and monitoring, effective management of identities and permissions, and implementing robust data protection measures across multi-cloud environments.
Organisations aiming to safeguard their cloud workloads against evolving threats stand to benefit the most from integrating security automation and consolidating telemetry data from disparate application and infrastructure security tools. This integration enables the creation of actionable, prioritised alerts based on enriched contextual data and analytics.
Looking Forward
By embracing advancements in consolidated tooling and seamlessly integrating data flows into existing processes, real-time risk posture management programs can offer continuous actionable guidance. Consequently, business leaders will possess the necessary insights to make informed risk decisions, informed by a holistic view of their multi-cloud security posture.
The efficacy of generative AI in enhancing cybersecurity hinges on its responsible and ethical deployment. As AI algorithms become increasingly sophisticated, there is a corresponding need for transparency and accountability to ensure that these systems operate in alignment with ethical principles and regulatory guidelines. Robust governance frameworks must be established to oversee the development and deployment of AI-driven cybersecurity solutions, mitigating the risk of unintended consequences or algorithmic biases.
