Keeping Pace With The Speed And Complexity Of Cyber Threats With Agentic MDR - Lanner Electronics | Network Appliance | uCPE SD-WAN| MEC Server | Intelligent Edge Appliance

Introduction

Today’s cyber adversaries no longer need advanced expertise to carry out attacks. Although highly skilled attackers have historically achieved greater success, modern technology, the widespread availability of sophisticated tools, and reduced costs have lowered the barrier to entry. Even less experienced actors can now exploit these resources to devise new and effective methods of breaching user accounts, penetrating infrastructure, moving laterally within networks, and extracting sensitive data—often within hours rather than days.

To counter this evolving landscape, agentic managed detection and response (MDR) providers must develop solutions that combine advanced security technologies with seasoned analysts and agentic AI-driven workflows must be introduced into a security information and event management platform so that AI engines can be leveraged to take advantage of newly integrated AI-powered capabilities to independently carry out key investigative processes that were once the responsibility of security operations analysts.

Challenges

A MDR service provider came to Lanner looking for a platform on which an AI-powered MDR solution can be built for enterprises struggling to manage multiple cybersecurity issues. Major challenges that must be resolved include:

• Manual Configurations: Decisions and configurations made by security teams could result in technology gaps. In many cases, using tools from multiple vendors adds complexity, making it harder for MDR and security analysts to handle the overwhelming number of alerts. Since the sheer scale of data is nearly impossible for humans to analyze for potential compromise indicators, the said MDR platforms must provide critical support that helps level the playing field.
• Inconsistencies: Beyond human-managed configurations, the reliance on expert-led triage and investigations adds variability and the potential for mistakes, which can result in significant costs.
   
• Repetition: Large Language Models (LLMs), not human interactions, should take over routine and slightly variable tasks by leveraging past data and continuously improving through user feedback.

Lanner Solution

The NCA-5550 is a high performance 1U rackmount network platform powered by Intel® Xeon® 6 SP Processor with P-cores and E-cores. The introduction of the NCA-5550 aims to satisfy network service providers’ demand for high-performing and robust appliances that are optimized for not only cloud-native, hyperscale and parallel tasks such as those found in network/edge applications, but also compute-intensive workloads such as artificial intelligence and HPC applications.

The IAC-PTL301A, on the other hand, is a FHFL PCIe smart NIC card inspired by the Intel NetSec Accelerator Reference Design. It features an Intel Xeon 6 SoC powered by the Intel Ethernet E830 Controller. Also equipped with Intel QAT, theIAC-PTL301A delivers hardware offloading for critical network security tasks, including AI, encryption, decryption, compression, decompression, and packet inspection.

The NCA-5550 and the IAC-PLT301A together make available the computational power required for real-time AI inference, enabling advanced deep learning capabilities for detecting cyber threats, identifying anomalies and mitigating zero-day vulnerabilities through a comprehensive process of data collection, AI model training, real-time detection and continuous learning.

Benefits

AI-driven workflows are designed to address the rapidly changing threat environment, where network attackers are adapt at launching quicker and more advanced attacks. Agentic MDR platforms are able to achieve almost 100% accuracy rate in alert triage, therefore freeing up hours of work per day for the on-site security analysts.

These workflows not only expand MDR capabilities and provide greater visibility into how decisions are made during the detection and investigation of security incidents but also add transparency to an ever-evolving and often challenging network security landscape inundated with ever-growing volume and complexity of alerts.

Agentic AI workflows are continuously improved through real-world application, strengthening organizations’ confidence in their security posture by delivering scalable, repeatable investigations while also freeing analysts to focus on more complex challenges. What’s more, the workflows are structured to provide clearer insight into the reasoning and decision-making of AI systems, giving organizations greater oversight of the security process and increased transparency when leveraging the platform.

Through agentic AI-driven workflows, artificial intelligence is leveraged to deliver the most relevant information, enabling analysts to act with speed and precision in identifying and stopping modern AI-powered threats. These workflows take on repetitive tasks, highlight the most meaningful findings and provide the necessary context to guide analysts in their decision-making.

By offering timely and actionable insights, agentic AI not only raises the overall quality of decisions but also gives analysts the confidence to move decisively to the next stage of the response process.

Conclusion

Agentic MDR systems are continuously refined through a combination of real-world operational data and insights from cybersecurity experts. This iterative approach is designed to deliver both scalability and flexibility in environments where attack techniques and volumes are constantly shifting. Prioritizing automation and transparency helps improve the accuracy of alerts, accelerates investigation timelines and allows security teams to deploy human expertise more strategically.

With AI-enhanced MDR services, organizations have greater control over their attack surfaces while keeping pace with the speed and complexity of AI-driven cyber threats, resulting in swift and decisive mitigation for the most credible threats.

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