Shaktiman Mall is Principal Product Manager at Aviatrix. With more than a decade of experience designing and implementing network solutions, Mall prides himself on ingenuity, creativity, adaptability and precision. Prior to joining Aviatrix, Mall served as Senior Technical Marketing Manager at Palo Alto Networks and Principal Infrastructure Engineer at MphasiS.
Aviatrix is a company focused on simplifying cloud networking to help businesses remain agile. Their cloud networking platform is used by over 500 enterprises and is designed to provide visibility, security, and control for adapting to changing needs. The Aviatrix Certified Engineer (ACE) Program offers certification in multicloud networking and security, aimed at supporting professionals in staying current with digital transformation trends.
What initially attracted you to computer engineering and cybersecurity?
As a student, I was initially more interested in studying medicine and wanted to pursue a degree in biotechnology. However, I decided to switch to computer science after having conversations with my classmates about technological advancements over the preceding decade and emerging technologies on the horizon.
Could you describe your current role at Aviatrix and share with us what your responsibilities are and what an average day looks like?
I’ve been with Aviatrix for two years and currently serve as a principal product manager in the product organization. As a product manager, my responsibilities include building product vision, conducting market research, and consulting with the sales, marketing and support teams. These inputs combined with direct customer engagement help me define and prioritize features and bug fixes.
I also ensure that our products align with customers’ requirements. New product features should be easy to use and not overly or unnecessarily complex. In my role, I also need to be mindful of the timing for these features – can we put engineering resources toward it today, or can it wait six months? To that end, should the rollout be staggered or phased into different versions? Most importantly, what is the projected return on investment?
An average day includes meetings with engineering, project planning, customer calls, and meetings with sales and support. Those discussions allow me to get an update on upcoming features and use cases while understanding current issues and feedback to troubleshoot before a release.
What are the primary challenges IT teams face when integrating AI tools into their existing cloud infrastructure?
Based on real-world experience of integrating AI into our IT technology, I believe there are four challenges companies will encounter:
- Harnessing data & integration: Data enriches AI, but when data is across different places and resources in an organization, it can be difficult to harness it properly.
- Scaling: AI operations can be CPU intensive, making scaling challenging.
- Training and raising awareness: A company could have the most powerful AI solution, but if employees don’t know how to use it or don’t understand it, then it will be underutilized.
- Cost: For IT especially, a quality AI integration will not be cheap, and businesses must budget accordingly.
- Security: Make sure that the cloud infrastructure meets security standards and regulatory requirements relevant to AI applications
How can businesses ensure their cloud infrastructure is robust enough to support the heavy computing needs of AI applications?
There are multiple factors to running AI applications. For starters, it’s critical to find the right type and instance for scale and performance.
Also, there needs to be adequate data storage, as these applications will draw from static data available within the company and build their own database of information. Data storage can be costly, forcing businesses to assess different types of storage optimization.
Another consideration is network bandwidth. If every employee in the company uses the same AI application at once, the network bandwidth needs to scale – otherwise, the application will be so slow as to be unusable. Likewise, companies need to decide if they will use a centralized AI model where computing happens in a single place or a distributed AI model where computing happens closer to the data sources.
With the increasing adoption of AI, how can IT teams protect their systems from the heightened risk of cyberattacks?
There are two main aspects to security every IT team must consider. First, how do we protect against external risks? Second, how do we ensure data, whether it is the personally identifiable information (PII) of customers or proprietary information, remains within the company and is not exposed? Businesses must determine who can and cannot access certain data. As a product manager, I need sensitive information others are not authorized to access or code.
At Aviatrix, we help our customers protect against attacks, allowing them to continue adopting technologies like AI that are essential for being competitive today. Recall network bandwidth optimization: because Aviatrix acts as the data plane for our customers, we can manage the data going through their network, providing visibility and enhancing security enforcement.
Likewise, our distributed cloud firewall (DCF) solves the challenges of a distributed AI model where data gets queried in multiple places, spanning geographical boundaries with different laws and compliances. Specifically, a DCF supports a single set of security compliance enforced across the globe, ensuring the same set of security and networking architecture is supported. Our Aviatrix Networks Architecture also allows us to identify choke points, where we can dynamically update the routing table or help customers create new connections to optimize AI requirements.
How can businesses optimize their cloud spending while implementing AI technologies, and what role does the Aviatrix platform play in this?
One of the main practices that will help businesses optimize their cloud spending when implementing AI is minimizing egress spend.
Cloud network data processing and egress fees are a material component of cloud costs. They are both difficult to understand and inflexible. These cost structures not only hinder scalability and data portability for enterprises, but also provide decreasing returns to scale as cloud data volume increases which can impact organizations’ bandwidth.
Aviatrix designed our egress solution to give the customer visibility and control. Not only do we perform enforcement on gateways through DCF, but we also do native orchestration, enforcing control at the network interface card level for significant cost savings. In fact, after crunching the numbers on egress spend, we had customers report savings between 20% and 40%.
We’re also building auto-rightsizing capabilities to automatically detect high resource utilization and automatically schedule upgrades as needed.
Lastly, we ensure optimal network performance with advanced networking capabilities like intelligent routing, traffic engineering and secure connectivity across multi-cloud environments.
How does Aviatrix CoPilot enhance operational efficiency and provide better visibility and control over AI deployments in multicloud environments?
Aviatrix CoPilot’s topology view provides real-time network latency and throughput, allowing customers to see the number of VPC/VNets. It also displays different cloud resources, accelerating problem identification. For example, if the customer sees a latency issue in a network, they will know which assets are getting affected. Also, Aviatrix CoPilot helps customers identify bottlenecks, configuration issues, and improper connections or network mapping. Furthermore, if a customer needs to scale up one of its gateways into the node to accommodate more AI capabilities, Aviatrix CoPilot can automatically detect, scale, and upgrade as necessary.
Can you explain how dynamic topology mapping and embedded security visibility in Aviatrix CoPilot assist in real-time troubleshooting of AI applications?
Aviatrix CoPilot’s dynamic topology mapping also facilitates robust troubleshooting capabilities. If a customer must troubleshoot an issue between different clouds (requiring them to understand where traffic was getting blocked), CoPilot can find it, streamlining resolution. Not only does Aviatrix CoPilot visualize network aspects, but it also provides security visualization components in the form of our own threat IQ, which performs security and vulnerability protection. We help our customers map the networking and security into one comprehensive visualization solution.
We also help with capacity planning for both cost with costIQ, and performance with auto right sizing and network optimization.
How does Aviatrix ensure data security and compliance across various cloud providers when integrating AI tools?
AWS and its AI engine, Amazon Bedrock, have different security requirements from Azure and Microsoft Copilot. Uniquely, Aviatrix can help our customers create an orchestration layer where we can automatically align security and network requirements to the CSP in question. For example, Aviatrix can automatically compartmentalize data for all CSPs irrespective of APIs or underlying architecture.
It is important to note that all of these AI engines are inside a public subnet, which means they have access to the internet, creating additional vulnerabilities because they consume proprietary data. Thankfully, our DCF can sit on a public and private subnet, ensuring security. Beyond public subnets, it can also sit across different regions and CSPs, between data centers and CSPs or VPC/VNets and even between a random site and the cloud. We establish end-to-end encryption across VPC/VNets and regions for secure transfer of data. We also have extensive auditing and logging for tasks performed on the system, as well as integrated network and policy with threat detection and deep packet inspection.
What future trends do you foresee in the intersection of AI and cloud computing, and how is Aviatrix preparing to address these trends?
I see the interaction of AI and cloud computing birthing incredible automation capabilities in key areas such as networking, security, visibility, and troubleshooting for significant cost savings and efficiency.
It could also analyze the different types of data entering the network and recommend the most suitable policies or security compliances. Similarly, if a customer needed to enforce HIPAA, this solution could scan through the customer’s networks and then recommend a corresponding strategy.
Troubleshooting is a major investment because it requires a call center to assist customers. However, most of these issues don’t necessitate human intervention.
Generative AI (GenAI) will also be a game changer for cloud computing. Today, a topology is a day-zero decision – once an architecture or networking topology gets built, it is difficult to make changes. One potential use case I believe is on the horizon is a solution that could recommend an optimal topology based on certain requirements. Another problem that GenAI could solve is related to security policies, which quickly become outdated after a few years. AGenAI solution could help users routinely create new security stacks per new laws and regulations.
Aviatrix can implement the same security architecture for a datacenter with our edge solution, given that more AI will sit close to the data sources. We can help connect branches and sites to the cloud and edge with AI computes running.
We also help in B2B integration with different customers or entities in the same company with separate operating models.
AI is driving new and exciting computing trends that will impact how infrastructure is built. At Aviatrix, we’re looking forward to seizing the moment with our secure and seamless cloud networking solution.
Thank you for the great interview, readers who wish to learn more should visit Aviatrix.
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