February 5, 2024
|
5
min read

Harness the power of AI and machine learning: Revolutionize data accessibility with ngrok

Mandy Hubbard

As artificial intelligence (AI) and machine learning (ML) reshape the technological scene, organizations seek to integrate these tools into their workflows to unlock new opportunities. The next wave of digital transformation will take us beyond the cloud to a world where every organization becomes an AI organization in order to move faster, drive value more efficiently, and compete effectively. 

The key to this new frontier of digital transformation lies in an organization’s ability to access and leverage vast amounts of data. In many cases, the mission critical data required to train large language models (LLMs) for AI lives in customer-owned networks, external to the environments where the apps that need to utilize this data operate. 

In this post, we’ll navigate the challenges of working with vast amounts of data in external networks and demonstrate how to overcome those challenges, paving the way for rapid innovation and growth for every organization seeking to adopt AI tooling.  

The challenge of accessing data in external networks

Traditionally, organizations working with AI and ML have faced significant challenges in accessing and processing customer data. The norm has been to transfer data to a centralized location, a process accompanied by high costs and potential security risks. However, we can feel a paradigm shift occurring as more organizations work directly within their customers' networks, accessing data and APIs in their native environments. This approach not only cuts down on data transfer costs but also fosters faster experimentation and iteration. 

To provide a deeper understanding, let's dissect the challenges organizations face in the journey to leverage AI and ML effectively.

Organizational approvals

To gain access to customer networks, you often must navigate an entangled web of bureaucracy. Each department—NetOps, DevOps, and SecOps—requires a unique set of protocols and comes with a diverse set of concerns. You must acquire each group’s approval, from both your customer’s organization and your own, which can be incredibly time consuming, often taking several months to get the green light. 

It requires a diplomatic approach balanced with technical expertise to align each team’s interests and address their concerns.

Technical configuration challenges

Once you get the green light, you’ll need to configure resources such as network firewalls, application firewalls, load balancers, VPC routing, certificate management, and more—all of which require specialized knowledge and often include a steep learning curve. Each customer network also comes with its unique configuration and security protocols. Adapting to these varied environments without compromising on efficiency and security poses a significant challenge.

Security and compliance hurdles

When accessing data directly within a customer’s environment, you must maintain the highest level of data security. You’ll need to implement robust encryption, secure data transfer channels, and strict access controls. You’ll also need to consider security compliance requirements, which differ between regions—such as GDPR in Europe and HIPPA in the US healthcare sector. Navigating these regulations and ensuring you adhere to them adds another layer of complexity to the process.

Time-to-value delays

These challenges can cause significant delays in project timelines. You may be faced with a long approval process involving cross-functional teams that span your organization and your customer’s. Once you complete the approval process, you’ll incur a hefty charge in both time and money to configure the network resources you need to gain access to your data. 

What could be a quick turnaround in an ideal scenario turns into a months-long process, diminishing the potential time-to-value of AI and ML initiatives. Prolonged development cycles not only delay the realization of benefits but also inflate the costs associated with AI and ML projects, affecting the overall return on investment. You must adopt an efficient mechanism for accessing data in external networks before you can reap the benefits that AI and ML offer. 

Dynamic technological landscape

With the field of AI and ML evolving at a breakneck pace, staying on top of the latest technologies, tools, and methodologies can be challenging. It can be especially daunting to research and discover new approaches while you’re drowning in the challenges presented above. Incorporating new AI and ML technologies into existing customer networks without disrupting their operations demands a high level of expertise and strategic planning.

By dissecting these challenges, we can dismantle them one at a time and gain a clearer understanding of what it takes to leverage AI and ML. This depth of understanding underscores the value of solutions like ngrok, which simplify and streamline the process of accessing data in external networks, enabling organizations to focus on innovation and value creation in the AI and ML space.

The ngrok solution for accessing data in customer networks

ngrok steps in to offer a game-changing solution that addresses these challenges head-on by providing secure ingress to external networks without the need for extensive network configuration changes in your customer’s environment. This innovative approach eliminates the need for a months-long, cross-organizational approval process, dramatically reducing the time-to-value for organizations.

With ngrok, you can access data in your customer’s network over an encrypted connection using mTLS and add additional security controls such as SSO, OAuth, OIDC, IP restrictions and more. Moreover, ngrok configures the network resources required to provide the secure connection—load balancers, firewalls, VPC routing, etc— in our global network, eliminating the need to acquire in-house expertise and dramatically shortening approval processes. By using ngrok to connect directly to external networks, organizations can tap into previously inaccessible data sources. This direct access allows for more efficient and cost-effective AI and ML solutions tailored to customer needs. 

The future of AI and ML with ngrok

The increasing significance of AI and ML in various sectors highlights the need for tools like ngrok. As we migrate workloads to robots and external devices outside the corporate network and SAAS vendors deliver solutions directly within Integrated Development Environments (IDEs), ngrok stands as a pivotal tool in this evolution. It enables seamless, secure, and rapid data access, fostering an environment where AI and ML can thrive.

No longer just a trend, the integration of AI and ML into business processes unlocks the power to compete in the digital age. ngrok, at the forefront of this revolution, provides the tools and capabilities needed to harness the full potential of AI and ML. By simplifying access to customer data and reducing the barriers to innovation, ngrok provides the catalyst for transformation in the ever-evolving world of AI and machine learning.

Join us in this journey of innovation and explore how ngrok can transform your organization's approach to AI and ML. Discover the power of seamless data accessibility and unlock the potential of your data today. You can sign up today and get started with ngrok. Check out these other posts on the ngrok blog to learn how ngrok unlocks the power of AI:

Connect with us on Twitter, the ngrok community on Slack, or contact us at support@ngrok.com.

Share this post
Mandy Hubbard
Mandy Hubbard is a seasoned technologist with a strong QA and developer advocacy background. She is passionate about software quality, CI/CD, good processes, and great documentation. Mandy is currently a Sr. Technical Marketing Engineer at ngrok, where she combines her technical experience and creative skills to help bring new features to customers.