Aditya K Sood (Ph.D) is the VP of Security Engineering and AI Strategy at Aryaka. With greater than 16 years of experience, he provides strategic leadership in information security, covering products and infrastructure. Dr. Sood is concerned about Artificial Intelligence (AI), cloud security, malware automation and evaluation, 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 answer is designed to mix performance, agility, security, and ease. Aryaka supports customers at various stages of their secure network access journey, assisting them in modernizing, optimizing, and remodeling their networking and security environments.
Are you able to tell us more about your journey in cybersecurity and AI and the way it led you to your current role at Aryaka?
My journey into cybersecurity and AI began with a fascination for technology’s potential to resolve complex problems. Early in my profession, 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 an ideal fit due to 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 deal with modern challenges like secure hybrid work, SD-WAN optimization, and real-time threat management. Aryaka’s convergence of AI and cybersecurity empowers organizations to remain ahead of threats while delivering exceptional network performance, and I’m thrilled to be an element of this mission.
As a thought leader in cybersecurity, how do you see AI reshaping the safety landscape in the following few years?
AI is on the point of transforming the cybersecurity landscape, relieving us of the burden of routine tasks and allowing us to concentrate on more complex challenges. Its ability to research vast datasets in real time enables security systems to discover anomalies, patterns, and emerging threats at a pace that surpasses human capabilities. AI/ML models repeatedly evolve, enhancing their accuracy in detecting and circumventing the impacts of advanced persistent threats (APTs) and zero-day vulnerabilities. Furthermore, AI is ready to revolutionize incident response (IR) by automating repetitive and time-sensitive tasks, comparable to isolating compromised systems or blocking malicious activities, significantly reducing response times and mitigating potential damage. As well as, AI will help bridge the cybersecurity skills gap by automating routine tasks and enhancing human decision-making, enabling security teams to focus on more complex challenges.
Nevertheless, adversaries quickly exploit the identical capabilities that make AI a strong defensive tool. Cybercriminals increasingly use AI to develop more sophisticated threats, comparable to deepfake phishing attacks, adaptive social engineering, and AI-driven malware. This trend will result in an ‘AI arms race,’ wherein organizations must repeatedly innovate to outpace these evolving threats.
What are the important thing networking challenges enterprises face when deploying AI applications, and why do you think these issues have gotten more critical?
As enterprises enterprise 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 a direct need for top bandwidth and ultra-low latency. For example, 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, resulting in frequent performance bottlenecks.
Scalability is a critical challenge in AI deployments. AI workloads’ dynamic and unpredictable nature necessitates networks that may 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 necessity for seamless data transfer and scaling across these environments is clear, however the complexity of achieving this without advanced networking solutions is equally apparent. Reliability can be paramount—AI systems often support mission-critical tasks, and even minor downtime or data loss can result in significant disruptions or flawed AI outputs.
Security and data integrity further complicate AI deployments. AI models depend 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 especially acute in industries with strict compliance requirements, comparable to healthcare and finance, where organizations need to satisfy regulatory obligations alongside performance needs.
As enterprises increasingly adopt AI, these networking challenges have gotten more critical, underscoring the necessity for advanced, AI-ready networking solutions that supply 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 brought on by data movement and the necessity for rapid decision-making?
Aryaka, with its intelligent, flexible, and optimized network management, is uniquely equipped to deal with the increased bandwidth and performance demands of AI workloads. The movement of enormous datasets between distributed locations, comparable to edge devices, data centers, and cloud environments, often significantly strains traditional networks. Aryaka’s solution provides relief by dynamically routing traffic across essentially 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, comparable to real-time data evaluation or machine learning model inference, Aryaka ensures that AI applications receive the obligatory network resources for rapid decision-making. Moreover, Aryaka’s solution supports dynamic bandwidth allocation, enabling enterprises to confidently scale resources up or down based on AI workload demands, stopping bottlenecks, and ensuring consistent performance even during peak usage.
Moreover, the Aryaka platform provides proactive monitoring and analytics capabilities, offering visibility into network performance and AI workload behaviors. This proactive approach allows enterprises to discover and resolve performance issues before they impact the operation of AI systems, ensuring uninterrupted operation. Combined with advanced safety 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 recent vulnerabilities or attack surfaces inside enterprise networks?
Adopting AI introduces recent vulnerabilities and attack surfaces inside enterprise networks as a consequence of 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 will probably result in breaches, model corruption, or compliance violations. Moreover, AI algorithms are vulnerable to adversarial attacks, where malicious actors introduce rigorously 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, resulting in 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, resulting in unintended outcomes or operational disruptions. For instance, attackers could manipulate data streams utilized by AI-driven monitoring systems, masking a security breach or generating false alarms to divert attention.
One other 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 possible entry point for attackers. Compromising an edge device, for example, could allow lateral movement across the network or provide a pathway to tamper with data being processed or transmitted to centralized AI systems. Moreover, unsecured APIs, often used for integrating AI applications, can expose vulnerabilities if not adequately protected.
As enterprises increasingly depend on AI for mission-critical functions, the potential consequences of those vulnerabilities turn out to be more severe, underscoring the urgent need for robust security measures. Organizations must act swiftly to deal with these challenges, comparable to 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 deal with 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 depend on. These measures safeguard AI data pipelines, stopping 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 attenuate 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 watch the safety and performance of AI workloads, proactively identifying potential malicious actions and dangerous behavior related to 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 within 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 comparable to prompt injections, information leakage, and abuse guardrails. Moreover, strict policies will be enforced to limit communication with unapproved and sanctioned GenAI services/applications. Furthermore, Aryaka addresses AI-specific security risks by implementing advanced strategies that mix networking and robust security measures. One critical approach is the adoption of Zero Trust Network Access (ZTNA), which enforces strict verification for each user, device, and application attempting to interact with AI workloads. It is important 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.
Are you able to share examples of how AI is getting used each to boost security and as a tool for potential network compromises?
AI plays an important role in cybersecurity. It is a strong tool for enhancing network security and a resource adversaries can exploit for classy attacks. Recognizing these applications underscores AI’s transformative potential within 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) which may go undetected by traditional methods. For instance, AI-powered systems can detect and mitigate Distributed Denial of Service (DDoS) attacks by analyzing network protocol patterns and responding mechanically to isolate malicious sources. Moreover, AI’s potential in behavioral analytics is important, 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 boost phishing campaigns, crafting compelling fake emails or messages tailored to individual targets through data scraping and evaluation. One alarming trend is deepfakes in social engineering. AI-generated audio or video convincingly impersonates executives or trusted individuals to control employees into divulging sensitive information or authorizing fraudulent transactions. Moreover, adversarial AI attacks goal other AI systems directly, introducing manipulated data to cause incorrect predictions or decisions that may disrupt critical operations reliant on AI-driven automation.
The twin uses of AI in cybersecurity underscore the importance of a proactive, multi-layered security strategy. While organizations must harness AI’s potential to boost their defenses, it’s equally crucial to stay 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 enterprise. Unlike legacy systems that depend on separate tools for networking (comparable to 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, no matter location. By leveraging a cloud-native architecture, Unified SASE eliminates the necessity 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, repeatedly 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 practical 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 especially critical for organizations embracing cloud applications and distant 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 contemporary, dynamic IT landscapes.
Are you able to 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 knowledge between distributed environments, comparable to 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 worldwide 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 related to public web connections. The architecture also employs advanced WAN optimization techniques, comparable to data deduplication and compression, to further enhance efficiency and reduce the strain on network resources. It is good for big datasets and frequent model updates related to AI operations, instilling confidence within 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 repeatedly monitored. Integrated safety features like Secure Web Gateway (SWG), Cloud Access Security Broker (CASB), and intrusion prevention systems (IPS) safeguard sensitive AI workloads against cyber threats. Moreover, by enabling edge-based policy enforcement, OnePASS™ minimizes latency while ensuring that security controls are applied consistently across distributed environments, providing a way of security within the system’s vigilance.
Aryaka’s single-pass architecture incorporates all essential security functions right into a unified platform. This integration allows real-time network traffic inspection and processing without requiring multiple security devices. This mix of secure, low-latency connectivity and robust threat protection makes Aryaka’s OnePASS™ architecture uniquely fitted to modern AI workloads.
What trends do you foresee in AI and network security as we move into 2025 and beyond?
As we glance towards 2025 and beyond, AI will play a pivotal role in network security. AI-powered threat detection systems will proceed to advance, leveraging AI/ML to discover patterns of malicious activity with unprecedented speed and accuracy. These systems will excel in detecting zero-day vulnerabilities and complicated attacks, comparable to advanced persistent threats (APTs). AI may also drive automation in incident response, a development that ought to reassure the audience in regards to 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. Moreover, as quantum computing evolves, it could undermine existing encryption standards in network security, pushing the industry toward quantum-safe cryptography.
Nevertheless, the growing integration of AI in network security brings challenges. Cybercriminals harness the ability of AI technologies to develop more advanced attacks, including phishing schemes and evasive malware. As a result of the risks of biased or improperly trained models, AI model vulnerabilities, which check with flaws within the design or implementation of AI systems, will likely increase. This may lead to exploiting AI models through newly discovered data poisoning and adversarial input manipulation techniques. As well as, adopting AI will improve the detection of security vulnerabilities in third-party libraries and packages utilized in software supply chains.
We also anticipate AI-driven tools will enable higher 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 constructing a worldwide cybersecurity framework for AI technologies.
