MalaysianUpdates

Tag: #AgenticAI

  • Cisco Redefines Security for the Era of the Agentic Workforce

    Cisco Redefines Security for the Era of the Agentic Workforce

    Cisco (NASDAQ: CSCO) has introduced a comprehensive suite of security innovations designed to support the rapidly evolving agentic AI ecosystem, where intelligent software agents are no longer limited to responding to queries but are increasingly सक्षम of taking action autonomously. Announced at the RSA Conference 2026, these solutions aim to address key barriers to enterprise adoption of AI agents by embedding robust security measures across every stage of the agent lifecycle—from identity and access control to deployment and real-time threat response.

    At the core of Cisco’s announcement is the expansion of Zero Trust Access to AI agents, enabling organisations to establish trusted identities, enforce strict access controls, and ensure full visibility over agentic activities. New capabilities within Cisco Identity Intelligence and Duo Identity and Access Management (IAM) allow enterprises to register and map AI agents to accountable human owners, while Secure Access security service edge (SSE) introduces model context protocol (MCP) policy enforcement and intent-aware monitoring. These advancements ensure that agents operate strictly within defined parameters, reducing the risk of misuse or unauthorised actions.

    To further strengthen AI security, Cisco has launched AI Defense: Explorer Edition, a self-service platform that empowers developers and security teams to test AI models and applications against real-world threats before deployment. Equipped with dynamic red teaming, model validation tools, and API-first integrations, the platform enables organisations to identify vulnerabilities such as prompt injection and jailbreak attempts while embedding robust guardrails into agent workflows. Complementing this is the introduction of the Agent Runtime Software Development Kit (SDK), which integrates policy enforcement directly into the development phase across major AI frameworks.

    Cisco also revealed DefenseClaw, an open-source secure agent framework designed to automate security processes and streamline deployment. By integrating tools such as Skills Scanner, MCP Scanner, AI Bill of Materials (BoM), and CodeGuard, DefenseClaw ensures that AI agents are fully verified, scanned, and inventoried before deployment. Its planned integration with NVIDIA OpenShell further enhances runtime security by providing a sandboxed environment that eliminates manual intervention and accelerates secure scaling of agentic workloads.

    In parallel, Cisco is advancing security operations through new AI-powered innovations within its Splunk platform. These include Exposure Analytics for real-time asset visibility and risk scoring, Detection Studio for streamlined threat detection workflows, and Federated Search for cross-environment data correlation. Additionally, a suite of specialised AI agents—such as Triage Agent, Malware Threat Reversing Agent, and Guided Response Agent—will automate and accelerate security operations, enabling Security Operations Centres (SOCs) to detect and respond to threats at machine speed.

    Cisco’s strategy is built around three key pillars: protecting the world from AI agents, protecting agents from external threats, and enabling rapid detection and response to AI-driven incidents. According to a recent Cisco survey, while 85% of enterprises are experimenting with AI agents, only 5% have deployed them at scale—highlighting the urgent need for trusted security frameworks.

    By embedding security into the foundation of the agentic AI economy, Cisco is positioning itself at the forefront of enabling safe and scalable AI adoption. As organisations continue to explore the transformative potential of AI agents, Cisco’s integrated approach aims to provide the confidence and control needed to unlock innovation while mitigating risk.

  • The AI Infrastructure Dilemma: Getting Ready for the Era of Agentic AI

    The AI Infrastructure Dilemma: Getting Ready for the Era of Agentic AI

    As the world races to establish leadership in Artificial Intelligence (AI), governments and industries are grappling with urgent demographic and economic pressures, from aging populations to shrinking workforces and the imperative to boost productivity. In this context, the emergence of agentic AI—systems that do more than respond, but reason, plan, and take autonomous actions—represents a transformative shift with profound implications for infrastructure, innovation, and national competitiveness. Unlike traditional AI models that answer queries, agentic AI can manage extended workflows independently: booking flights, updating schedules, sending reminders, or adapting plans dynamically based on external factors. This proactive, collaborative intelligence will require significantly more compute power, not only to process individual tasks but to orchestrate reasoning, planning, and continuous adaptation across billions of simultaneous users and applications.

    The challenge extends beyond GPUs, which have traditionally dominated AI conversations for training and inference. CPUs, high-speed interconnects, memory, and networking form the backbone of modern AI infrastructure, coordinating workloads, managing data movement, and supporting real-time system orchestration. High-performance CPUs, such as AMD EPYC™ 9005 Series, are critical for running AI workloads efficiently, particularly as models evolve into more modular and distributed architectures like mixture-of-experts systems. Connectivity is equally vital: smart network interface controllers (NICs), low-latency interconnects, and scalable fabrics ensure seamless, high-throughput data flow, enabling agentic AI to operate at scale with minimal delays. The convergence of these components into heterogeneous, rack-scale systems is essential for orchestrating complex, real-time interactions between billions of AI agents.

    Openness in software, hardware, and systems design emerges as another strategic priority. Closed ecosystems risk vendor lock-in and limit flexibility at a time when agility is crucial to scale AI. Open software stacks like AMD ROCm™ enable developers and researchers to build, optimize, and deploy AI models across diverse environments, supporting popular frameworks like PyTorch and TensorFlow while offering portability and performance tuning. Open hardware standards, including the Open Compute Project (OCP), the Ultra Accelerator Link (UALink), and next-generation networking frameworks from the Ultra Ethernet Consortium (UEC), provide the modularity, high-bandwidth connectivity, and interoperability needed for distributed AI systems. These open initiatives empower cloud and data center operators to build flexible, energy-efficient infrastructure capable of supporting both global AI innovation and local differentiation.

    For countries like Malaysia, embracing an open, heterogeneous AI ecosystem is not simply a technical decision—it is a strategic imperative. National competitiveness in the age of agentic AI depends on the ability to deploy scalable, high-performance infrastructure that supports complex workloads, facilitates local innovation, and ensures technological sovereignty. The upcoming release of AMD’s Helios, a next-generation rack-scale reference design, exemplifies the integration of high-performance compute, open software, and scalable architecture necessary to meet the demands of agentic AI in 2026 and beyond.

    Looking ahead, the successful adoption of agentic AI requires a holistic approach to infrastructure: CPUs and GPUs must work in tandem, high-speed networking and interconnects must provide low-latency data movement, and open software and modular rack-scale systems must enable flexibility, innovation, and interoperability. By investing in such infrastructure, Malaysia and other nations can harness the transformative power of agentic AI to drive automation, innovation, and sustainable economic growth while navigating the global AI race.