Aditya K Sood (Ph.D) is the VP of Security Engineering and AI Strategy at Aryaka. With more than 16 years of experience, he provides strategic leadership in information security, covering products and infrastructure. Dr. Sood is interested in Artificial Intelligence (AI), cloud security, malware automation and analysis, application security, and secure software design. He has authored several papers for various magazines and journals, including IEEE, Elsevier, Crosstalk, ISACA, Virus Bulletin, and Usenix.
Aryaka provides network and security solutions, offering Unified SASE as a Service. The solution is designed to combine performance, agility, security, and simplicity. Aryaka supports customers at various stages of their secure network access journey, assisting them in modernizing, optimizing, and transforming their networking and security environments.
Can you tell us more about your journey in cybersecurity and AI and how it led you to your current role at Aryaka?
My journey into cybersecurity and AI began with a fascination for technology’s potential to solve complex problems. Early in my career, I focused on cybersecurity, threat intelligence, and security engineering, which gave me a solid foundation in understanding how systems interact and where vulnerabilities might lie. This exposure naturally led me to delve deeper into cybersecurity, where I recognized the critical importance of safeguarding data and networks in an increasingly interconnected world. As AI technologies emerged, I saw their immense potential for transforming cybersecurity—from automating threat detection to predictive analytics.
Joining Aryaka as VP of Security Engineering and AI Strategy was a perfect fit because of its leadership in Unified SASE as a Service, cloud-first WAN solutions, and innovation focus. My role allows me to synthesize my passion for cybersecurity and AI to address modern challenges like secure hybrid work, SD-WAN optimization, and real-time threat management. Aryaka’s convergence of AI and cybersecurity empowers organizations to stay ahead of threats while delivering exceptional network performance, and I’m thrilled to be a part of this mission.
As a thought leader in cybersecurity, how do you see AI reshaping the security landscape in the next few years?
AI is on the brink of transforming the cybersecurity landscape, relieving us of the burden of routine tasks and allowing us to focus on more complex challenges. Its ability to analyze vast datasets in real time enables security systems to identify anomalies, patterns, and emerging threats at a pace that surpasses human capabilities. AI/ML models continuously evolve, enhancing their accuracy in detecting and circumventing the impacts of advanced persistent threats (APTs) and zero-day vulnerabilities. Moreover, AI is set to revolutionize incident response (IR) by automating repetitive and time-sensitive tasks, such as isolating compromised systems or blocking malicious activities, significantly reducing response times and mitigating potential damage. In addition, AI will help bridge the cybersecurity skills gap by automating routine tasks and enhancing human decision-making, enabling security teams to concentrate on more complex challenges.
However, adversaries quickly exploit the same capabilities that make AI a powerful defensive tool. Cybercriminals increasingly use AI to develop more sophisticated threats, such as deepfake phishing attacks, adaptive social engineering, and AI-driven malware. This trend will lead to an ‘AI arms race,’ in which organizations must continuously innovate to outpace these evolving threats.
What are the key networking challenges enterprises face when deploying AI applications, and why do you believe these issues are becoming more critical?
As enterprises venture into AI applications, they face urgent networking challenges. The demanding nature of AI workloads, which involve transferring and processing massive datasets in real-time, particularly for processing and learning tasks, creates an immediate need for high bandwidth and ultra-low latency. For instance, real-time AI applications like autonomous systems or predictive analytics hinge on instantaneous data processing, where even the slightest delays can disrupt outcomes. These demands often surpass the capabilities of traditional network infrastructures, leading to frequent performance bottlenecks.
Scalability is a critical challenge in AI deployments. AI workloads’ dynamic and unpredictable nature necessitates networks that can swiftly adapt to changing resource requirements. Enterprises deploying AI across hybrid or multi-cloud environments face added complexity as data and workloads are distributed across diverse locations. The need for seamless data transfer and scaling across these environments is evident, but the complexity of achieving this without advanced networking solutions is equally apparent. Reliability is also paramount—AI systems often support mission-critical tasks, and even minor downtime or data loss can lead to significant disruptions or flawed AI outputs.
Security and data integrity further complicate AI deployments. AI models rely on vast amounts of sensitive data for training and inference, making secure data transfer and protection against breaches or manipulation a top priority. This challenge is particularly acute in industries with strict compliance requirements, such as healthcare and finance, where organizations need to meet regulatory obligations alongside performance needs.
As enterprises increasingly adopt AI, these networking challenges are becoming more critical, underscoring the need for advanced, AI-ready networking solutions that offer high bandwidth, low latency, scalability, and robust security.
How does Aryaka’s platform address the increased bandwidth and performance demands of AI workloads, particularly in managing the strain caused by data movement and the need for rapid decision-making?
Aryaka, with its intelligent, flexible, and optimized network management, is uniquely equipped to address the increased bandwidth and performance demands of AI workloads. The movement of large datasets between distributed locations, such as edge devices, data centers, and cloud environments, often significantly strains traditional networks. Aryaka’s solution provides relief by dynamically routing traffic across the most efficient and available paths, leveraging multiple connectivity options to optimize bandwidth and reduce latency.
One key advantage of Aryaka’s solution is its ability to prioritize critical AI-related traffic through application-aware routing. By identifying and prioritizing latency-sensitive workloads, such as real-time data analysis or machine learning model inference, Aryaka ensures that AI applications receive the necessary network resources for rapid decision-making. Additionally, Aryaka’s solution supports dynamic bandwidth allocation, enabling enterprises to confidently scale resources up or down based on AI workload demands, preventing bottlenecks, and ensuring consistent performance even during peak usage.
Furthermore, the Aryaka platform provides proactive monitoring and analytics capabilities, offering visibility into network performance and AI workload behaviors. This proactive approach allows enterprises to identify and resolve performance issues before they impact the operation of AI systems, ensuring uninterrupted operation. Combined with advanced security features like CASB, SWG, FWaaS, end-to-end encryption, ZTNA, and others, Aryaka platforms safeguard the integrity of AI data.
How does AI adoption introduce new vulnerabilities or attack surfaces within enterprise networks?
Adopting AI introduces new vulnerabilities and attack surfaces within enterprise networks due to the unique ways AI systems operate and interact with data. One significant risk comes from the vast amounts of sensitive data that AI systems require for training and inference. If this data is intercepted, manipulated, or stolen during transfer or storage, it can lead to breaches, model corruption, or compliance violations. Additionally, AI algorithms are susceptible to adversarial attacks, where malicious actors introduce carefully crafted inputs (e.g., altered images or data) designed to mislead AI systems into making incorrect decisions. These attacks can compromise critical applications like fraud detection or autonomous systems, leading to severe operational or reputational damage. AI adoption also introduces risks related to automation and decision-making. Malicious actors can exploit automated decision-making systems by feeding them false data, leading to unintended outcomes or operational disruptions. For example, attackers could manipulate data streams used by AI-driven monitoring systems, masking a security breach or generating false alarms to divert attention.
Another challenge arises from the complexity and distributed nature of AI workloads. AI systems often involve interconnected components across edge devices, cloud platforms, and infrastructure. This intricate web of interconnectedness significantly expands the attack surface, as each element and communication pathway represents a potential entry point for attackers. Compromising an edge device, for instance, could allow lateral movement across the network or provide a pathway to tamper with data being processed or transmitted to centralized AI systems. Furthermore, unsecured APIs, often used for integrating AI applications, can expose vulnerabilities if not adequately protected.
As enterprises increasingly rely on AI for mission-critical functions, the potential consequences of these vulnerabilities become more severe, underscoring the urgent need for robust security measures. Organizations must act swiftly to address these challenges, such as adversarial training for AI models, securing data pipelines, and adopting zero-trust architectures to safeguard AI-driven environments.
What strategies or technologies are you implementing at Aryaka to address these AI-specific security risks?
The Aryaka platform uses end-to-end encryption for data in transit and at rest to secure the vast amounts of sensitive data AI systems rely on. These measures safeguard AI data pipelines, preventing interception or manipulation during transfer between edge devices, data centers, and cloud services. Dynamic traffic routing further enhances security and performance by directing AI-related traffic through secure and efficient paths while prioritizing critical workloads to minimize latency and ensure reliable decision-making.
Aryaka’s AI Observe solution monitors network traffic by analyzing logs for suspicious activity. Centralized visibility and analytics provided by Aryaka enable organizations to monitor the security and performance of AI workloads, proactively identifying potential malicious actions and risky behavior associated with end users, including critical servers and hosts. AI Observe utilizes AI/ML algorithms to trigger security incident notifications based on the severity calculated using various parameters and variables for decision-making.
Aryaka’s AI>Secure inline network solution, coming in the second half of 2025, will enable organizations to dissect the traffic between end users and AI services endpoints (ChatGPT, Gemini, copilot, etc.) to uncover attacks such as prompt injections, information leakage, and abuse guardrails. Additionally, strict policies can be enforced to restrict communication with unapproved and sanctioned GenAI services/applications. Moreover, Aryaka addresses AI-specific security risks by implementing advanced strategies that combine networking and robust security measures. One critical approach is the adoption of Zero Trust Network Access (ZTNA), which enforces strict verification for every user, device, and application attempting to interact with AI workloads. It is essential in distributed AI environments, where workloads span edge devices, cloud platforms, and on-premises infrastructure, making them vulnerable to unauthorized access and lateral movement by attackers.
By employing these comprehensive measures, Aryaka helps enterprises secure their AI environments against evolving risks while enabling scalable and efficient AI deployment.
Can you share examples of how AI is being used both to enhance security and as a tool for potential network compromises?
AI plays a crucial role in cybersecurity. It is a robust tool for enhancing network security and a resource adversaries can exploit for sophisticated attacks. Recognizing these applications underscores AI’s transformative potential in the cybersecurity landscape and empowers us to navigate the risks it introduces.
AI is revolutionizing network security through advanced threat detection and prevention. AI models analyze vast amounts of network traffic in real time, identifying anomalies, suspicious behavior, or indicators of compromise (IOCs) that might go undetected by traditional methods. For example, AI-powered systems can detect and mitigate Distributed Denial of Service (DDoS) attacks by analyzing network protocol patterns and responding automatically to isolate malicious sources. Additionally, AI’s potential in behavioral analytics is significant, creating profiles of normal user behavior to detect insider threats or account compromises. But its most potent application is predictive analytics, where AI systems forecast potential vulnerabilities or attack vectors, enabling proactive defenses before threats materialize.
Conversely, cybercriminals are leveraging AI to develop more sophisticated attacks. AI-driven malicious code can adapt to evade traditional detection mechanisms by changing its characteristics dynamically. Attackers also use AI/ML to enhance phishing campaigns, crafting compelling fake emails or messages tailored to individual targets through data scraping and analysis. One alarming trend is deepfakes in social engineering. AI-generated audio or video convincingly impersonates executives or trusted individuals to manipulate employees into divulging sensitive information or authorizing fraudulent transactions. Furthermore, adversarial AI attacks target other AI systems directly, introducing manipulated data to cause incorrect predictions or decisions that can disrupt critical operations reliant on AI-driven automation.
The dual uses of AI in cybersecurity underscore the importance of a proactive, multi-layered security strategy. While organizations must harness AI’s potential to enhance their defenses, it’s equally crucial to remain vigilant against potential misuse.
How does Aryaka’s Unified SASE as a Service stand out from traditional network and security solutions?
Aryaka’s Unified SASE as a Service solution is designed to scale with your business. Unlike legacy systems that rely on separate tools for networking (such as MPLS) and security (like firewalls and VPNs), Unified SASE integrates these functions, offering a seamless and scalable solution. This convergence simplifies management and provides consistent security policies and performance for users, regardless of location. By leveraging a cloud-native architecture, Unified SASE eliminates the need for complex on-premises hardware, reduces costs, and enables businesses to adapt quickly to modern hybrid work environments.
A key differentiator of Aryaka is its ability to support Zero Trust (ZT) principles at scale. It enforces identity-based access controls, continuously verifying user and device trustworthiness before granting access to resources. Combined with capabilities like Secure Web Gateways (SWG), Cloud Access Security Broker (CASB), Intrusion Detection and Prevention Systems (IDPS), Next-Gen Firewalls (NGFW), and networking functions, Aryaka provides robust protection against threats while safeguarding sensitive data across distributed environments. Its ability to integrate AI further enhances threat detection and response, ensuring faster and more effective mitigation of security incidents.
Aryaka enhances user experience and performance. Unified SASE leverages Software-Defined Wide Area Networking (SD-WAN) to optimize traffic routing, ensuring low latency and high-speed connections. This is particularly critical for organizations embracing cloud applications and remote work. By delivering security and performance from a unified platform, Unified SASE minimizes complexity, improves scalability, and ensures that organizations can meet the demands of modern, dynamic IT landscapes.
Can you explain how Aryaka’s OnePASS™ architecture supports AI workloads while ensuring secure and efficient data transmission?
Aryaka’s OnePASS™ architecture supports AI workloads by integrating secure, high-performance network connectivity with robust security and data optimization features. AI workloads often transmit large volumes of data between distributed environments, such as edge devices, data centers, and cloud-based AI platforms. OnePASS™ ensures that these data flows are efficient and secure by leveraging Aryaka’s global private backbone and Secure Access Service Edge (SASE) capabilities.
The global private backbone provides low-latency, high-bandwidth connectivity, which is critical for AI workloads requiring real-time data processing and decision-making. This optimized network ensures fast and reliable data transmission, avoiding the bottlenecks commonly associated with public internet connections. The architecture also employs advanced WAN optimization techniques, such as data deduplication and compression, to further enhance efficiency and reduce the strain on network resources. It is ideal for large datasets and frequent model updates associated with AI operations, instilling confidence in the system’s performance.
From a security perspective, Aryaka’s OnePASS™ architecture enforces a Zero Trust framework, ensuring all data flows are authenticated, encrypted, and continuously monitored. Integrated security features like Secure Web Gateway (SWG), Cloud Access Security Broker (CASB), and intrusion prevention systems (IPS) safeguard sensitive AI workloads against cyber threats. Additionally, by enabling edge-based policy enforcement, OnePASS™ minimizes latency while ensuring that security controls are applied consistently across distributed environments, providing a sense of security in the system’s vigilance.
Aryaka’s single-pass architecture incorporates all essential security functions into a unified platform. This integration allows real-time network traffic inspection and processing without requiring multiple security devices. This combination of secure, low-latency connectivity and robust threat protection makes Aryaka’s OnePASS™ architecture uniquely suited for modern AI workloads.
What trends do you foresee in AI and network security as we move into 2025 and beyond?
As we look towards 2025 and beyond, AI will play a pivotal role in network security. AI-powered threat detection systems will continue to advance, leveraging AI/ML to identify patterns of malicious activity with unprecedented speed and accuracy. These systems will excel in detecting zero-day vulnerabilities and sophisticated attacks, such as advanced persistent threats (APTs). AI will also drive automation in incident response, a development that should reassure the audience about the efficiency of future security systems. This automation will enable Security Orchestration, Automation, and Response (SOAR) systems to neutralize threats autonomously, minimizing response times and reducing the burden on human analysts. Additionally, as quantum computing evolves, it could undermine existing encryption standards in network security, pushing the industry toward quantum-safe cryptography.
However, the growing integration of AI in network security brings challenges. Cybercriminals harness the power of AI technologies to develop more advanced attacks, including phishing schemes and evasive malware. Due to the risks of biased or improperly trained models, AI model vulnerabilities, which refer to flaws in the design or implementation of AI systems, will likely increase. This will result in exploiting AI models through newly discovered data poisoning and adversarial input manipulation techniques. In addition, adopting AI will improve the detection of security vulnerabilities in third-party libraries and packages used in software supply chains.
We also anticipate AI-driven tools will enable better collaboration between security tools, teams, and organizations. AI-centric solutions will create personalized security models, making the audience feel that their security needs are being met. These models will create individualized security policies based on user roles and behavior. Nation-states will collaborate on building a global cybersecurity framework for AI technologies.
Thank you for the great interview, readers who wish to learn more should visit Aryaka.
