Securing the Generative AI Frontier in the Age of Unified SASE

Generative AI models, trained on vast amounts of sensitive data, pose significant security risks

Ken Rutsky, Chief marketing officer at Aryaka

October 30, 2024

4 Min Read
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Imagine trying to drive on an extremely sunny day without sunglasses. The bright light would be overwhelming, making it difficult to navigate and see the road ahead. Similarly, organizations that approach generative AI without the right tools and strategies, risk being blinded by the potential benefits while overlooking the very real challenges in front of them.

The widespread adoption of cloud-based applications has expanded organizations' attack surfaces, making them increasingly vulnerable to cyberattacks and network degradation. To address these challenges, Secure Access Service Edge (SASE) architectures have become essential for today's enterprises. However, generative AI introduces a whole new set of complexities and vulnerabilities in SASE environments.

Generative AI models, trained on vast amounts of sensitive data, pose significant security risks. According to a recent report from generative AI security company Lakera, 95% of cybersecurity experts express low confidence in generative AI security measures. Protecting this data from breaches, unauthorized access and exfiltration is crucial. Enterprises must ensure strict control over data input and access to generative AI systems, which is easier said than done. Without proper safeguards in place, organizations risk exposing intellectual property and sensitive information, potentially causing severe damage to their business operations and reputation.

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Network Challenges of Generative AI

In addition to the data security challenges, generative AI also presents unique obstacles for network infrastructure. Generative AI workloads, particularly large language models (LLMs), demand massive data transfers that can strain network capacity. This increased traffic can lead to network congestion, latency and decreased application performance, impacting overall user experience and business operations.

According to a recent Goldman Sachs report, on average, a ChatGPT query requires nearly 10 times as much electricity to process as a Google search. The resulting surge in traffic can overwhelm traditional network architectures, leading to unreliable performance and potential security vulnerabilities.

The ability to deliver consistent performance across different network environments is crucial for supporting generative AI applications globally. Given how they’re set up today, many organizations leveraging generative AI for innovation will face significant networking and security challenges in delivering these workloads efficiently and securely at scale.

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Preparing Networks for Generative AI: The Role of SASE

While there’s no one-size-fits-all answer to solve all generative AI network and security issues, the best place to start is at the foundational level. Since generative AI poses unprecedented risks to both networks and security, organizations can benefit from adopting a holistic approach that puts equal value on networking performance and security. Traditionally, enterprises were forced to choose between performance and security, but as modern, unified SASE solutions continue to evolve with market demands, that is no longer the only option.

Modern, unified SASE platforms that are capable of intelligent orchestration and continuous optimization, can strike a good balance between high performance and robust security. These platforms can also deliver low latency and global reach, which is essential for supporting demanding generative AI workloads. Additionally, they offer the ability to rapidly scale resources and centrally manage network and security infrastructure, crucial for growing increasingly complicated generative AI initiatives. To safeguard sensitive generative AI data and models, advanced security measures like data loss prevention are seamlessly embedded into these platforms.

On the other hand, traditional SASE solutions may not be fully equipped to handle the unique challenges posed by generative AI workloads. For example, some SASE platforms may lack the advanced orchestration and optimization capabilities needed to manage the complex and dynamic nature of generative AI traffic. Additionally, older SASE solutions were not likely designed with the specific security requirements of generative AI in mind, leaving organizations vulnerable to new threats.

The Path Forward

The potential of generative AI presents a significant opportunity for organizations across the globe to further enhance business outcomes and drive incredible innovation. However, fully realizing that success requires a proactive approach to addressing the unique network and security challenges posed by generative AI. This begins with conducting performance audits and security assessments to identify potential weaknesses and implementing the necessary improvements before they become a problem.

It’s also important to remember that what may work today may not work in a few months from now. As generative AI technology continues to evolve rapidly, it’s crucial to ensure that networks can not only support current initiatives but also adapt to future advancements and keep organizations protected. Those that are taking a proactive approach with SASE solutions built for the complexities of generative AI, can unlock the full potential of this technology without sacrificing performance, security and reliability along the way.

The future is bright. Don’t forget to put on your sunglasses first.

About the Author

Ken Rutsky

Chief marketing officer at Aryaka, Aryaka

As chief marketing officer, Ken is responsible for worldwide marketing strategy, programs and execution to build Aryaka’s leadership position and go to market success.  Ken is a Silicon Valley marketing leader with a proven ability to build categories and brands and drive business growth. His experience spans industry giants like Intel, Netscape and McAfee, where he drove the marketing that put the Secure Web Gateway business on a trajectory to grow from $50 million to over $300 million in just three years. Before joining Aryaka, Ken ran a successful go-to-market consulting practice where he helped create over $15 billion in market valuation including IPOs and successful exits for over a dozen clients.

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