Shaktiman Mall is Principal Product Manager at Aviatrix. With greater 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 an organization focused on simplifying cloud networking to assist businesses remain agile. Their cloud networking platform is utilized by over 500 enterprises and is designed to offer visibility, security, and control for adapting to changing needs. The Aviatrix Certified Engineer (ACE) Program offers certification in multicloud networking and security, geared toward supporting professionals in staying current with digital transformation trends.
What initially attracted you to computer engineering and cybersecurity?
As a student, I used to be initially more fascinated with studying medicine and desired to pursue a level in biotechnology. Nonetheless, I made a decision to modify 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 a mean day looks like?
I’ve been with Aviatrix for 2 years and currently function a principal product manager within the product organization. As a product manager, my responsibilities include constructing 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 be sure that our products align with customers’ requirements. Latest product features ought to be easy to make use of and never overly or unnecessarily complex. In my role, I also must 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 significantly, what’s the projected return on investment?
A median 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 first 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 consider there are 4 challenges firms will encounter:
- Harnessing data & integration: Data enriches AI, but when data is across different places and resources in a corporation, it could be difficult to harness it properly.
- Scaling: AI operations will be CPU intensive, making scaling difficult.
- Training and raising awareness: An organization could have probably the most powerful AI solution, but when employees don’t know how one can use it or don’t understand it, then it can be underutilized.
- Cost: For IT especially, a high quality AI integration won’t be low cost, and businesses must budget accordingly.
- Security: Be certain that that the cloud infrastructure meets security standards and regulatory requirements relevant to AI applications
How can businesses ensure their cloud infrastructure is powerful enough to support the heavy computing needs of AI applications?
There are multiple aspects to running AI applications. For starters, it’s critical to search out the appropriate type and instance for scale and performance.
Also, there must be adequate data storage, as these applications will draw from static data available inside the company and construct their very own database of data. Data storage will be costly, forcing businesses to evaluate several types of storage optimization.
One other consideration is network bandwidth. If every worker in the corporate uses the identical AI application without delay, the network bandwidth must scale – otherwise, the appliance shall be so slow as to be unusable. Likewise, firms need to choose if they are going to use a centralized AI model where computing happens in a single place or a distributed AI model where computing happens closer to the information sources.
With the increasing adoption of AI, how can IT teams protect their systems from the heightened risk of cyberattacks?
There are two primary facets to security every IT team must consider. First, how will we protect against external risks? Second, how will we ensure data, whether it’s the personally identifiable information (PII) of shoppers or proprietary information, stays inside the company and shouldn’t be exposed? Businesses must determine who can and can’t access certain data. As a product manager, I would like sensitive information others usually are not authorized to access or code.
At Aviatrix, we help our customers protect against attacks, allowing them to proceed adopting technologies like AI which can be essential for being competitive today. Recall network bandwidth optimization: because Aviatrix acts as the information plane for our customers, we will manage the information 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 identical set of security and networking architecture is supported. Our Aviatrix Networks Architecture also allows us to discover choke points, where we will dynamically update the routing table or help customers create latest connections to optimize AI requirements.
How can businesses optimize their cloud spending while implementing AI technologies, and what role does the Aviatrix platform play on this?
One in every of the primary practices that may help businesses optimize their cloud spending when implementing AI is minimizing egress spend.
Cloud network data processing and egress fees are a cloth component of cloud costs. They’re each obscure and inflexible. These cost structures not only hinder scalability and data portability for enterprises, but additionally provide decreasing returns to scale as cloud data volume increases which might impact organizations’ bandwidth.
Aviatrix designed our egress solution to offer the client visibility and control. Not only will we perform enforcement on gateways through DCF, but we also do native orchestration, enforcing control on the network interface card level for significant cost savings. In truth, after crunching the numbers on egress spend, we had customers report savings between 20% and 40%.
We’re also constructing auto-rightsizing capabilities to mechanically detect high resource utilization and mechanically 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 supply higher 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 variety of VPC/VNets. It also displays different cloud resources, accelerating problem identification. For instance, if the client sees a latency issue in a network, they are going to know which assets are getting affected. Also, Aviatrix CoPilot helps customers discover bottlenecks, configuration issues, and improper connections or network mapping. Moreover, if a customer must scale up certainly one of its gateways into the node to accommodate more AI capabilities, Aviatrix CoPilot can mechanically detect, scale, and upgrade as obligatory.
Are you able to 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 a problem between different clouds (requiring them to grasp where traffic was getting blocked), CoPilot can find it, streamlining resolution. Not only does Aviatrix CoPilot visualize network facets, however it also provides security visualization components in the shape 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 capability planning for each 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 may help our customers create an orchestration layer where we will mechanically align security and network requirements to the CSP in query. For instance, Aviatrix can mechanically compartmentalize data for all CSPs regardless of APIs or underlying architecture.
It is crucial to notice that each one of those AI engines are inside a public subnet, which implies they’ve access to the web, creating additional vulnerabilities because they devour proprietary data. Thankfully, our DCF can sit on a private and non-private subnet, ensuring security. Beyond public subnets, it could 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 information. We even have extensive auditing and logging for tasks performed on the system, in addition to integrated network and policy with threat detection and deep packet inspection.
What future trends do you foresee within the intersection of AI and cloud computing, and the way is Aviatrix preparing to handle these trends?
I see the interaction of AI and cloud computing birthing incredible automation capabilities in key areas reminiscent of networking, security, visibility, and troubleshooting for significant cost savings and efficiency.
It could also analyze the differing types of information entering the network and recommend probably the most suitable policies or security compliances. Similarly, if a customer needed to implement HIPAA, this solution could scan through the client’s networks after which recommend a corresponding strategy.
Troubleshooting is a serious investment since it requires a call center to help customers. Nonetheless, most of those issues don’t necessitate human intervention.
Generative AI (GenAI) may 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’s difficult to make changes. One potential use case I consider is on the horizon is an answer that might recommend an optimal topology based on certain requirements. One other problem that GenAI could solve is expounded to security policies, which quickly change into outdated after a couple of years. AGenAI solution could help users routinely create latest security stacks per latest laws and regulations.
Aviatrix can implement the identical security architecture for a datacenter with our edge solution, provided that more AI will sit near the information sources. We may help connect branches and sites to the cloud and edge with AI computes running.
We also assist in B2B integration with different customers or entities in the identical company with separate operating models.
AI is driving latest and exciting computing trends that may impact how infrastructure is built. At Aviatrix, we’re looking forward to seizing the moment with our secure and seamless cloud networking solution.
