Harnessing The Power of AI in Networking and Security
The arrival of Artificial Intelligence (AI) and its subset, Large Language Models (LLMs) and GenAI applications has significantly impacted every industry, including enterprise networking and security. This technological advancement, akin to the advent of the Internet, mobile, and cloud, is transformative and promises profound changes in the coming decade.
CEO and Chair of the Board of Directors,
Aryaka
AI’s Transformational Power – Two Sides of the Coin
AI presents endless opportunities for enterprises, drastically reducing human effort by automating tasks like network configuration optimization and operational insights collection. A prime example is AI-enabled network management, which automates workflows, optimizes network operations, and enhances reliability by analyzing large data sets to detect patterns and anomalies, thus reducing downtime. In security, AI leverages LLMs to process data at scale, detect anomalies, predict threats, and automate responses. AI enables organizations to design models to inspect historical data and current trends to unveil risks in a proactive manner to eradicate any threats to businesses. AI reduces the dwell time of attackers and mitigate damage by empowering organizations to respond more effectively to security breaches through rapid detection, automated incident analysis, enhanced forensic capabilities, predictive insights, automated remediation, and continuous improvement.
However, AI also benefits cybercriminals, who use AI to create sophisticated threats like AI-driven malware and AI-powered phishing attacks. This dual-use nature necessitates robust and adaptive security measures to counter advanced threats.
AI Implementation in Networking and Security
Networking and Security have critical roles in helping organizations leverage AI fully, and organizations must consider several factors:
- Data Hygiene and Integration: High-quality data is essential for AI systems. This requires accurate, consistent, and integrated data from various sources, with the network being the single source of truth.
- Flexible and Scalable Capacity: AI solutions must be scalable to meet growth and business needs. Cloud-based networking and security offer the necessary scalability for AI services.
- Skill Development and Training: Staff must be trained in AI technology, prompt use, and data analytics to effectively employ AI tools and interpret results.
- Privacy Concerns: Enterprises must establish explicit data protection policies for AI use ensuring security and compliance with regulations like the EU’s GDPR and India’s DPDPA.
- Vendor Selection: Organizations should evaluate vendors based on their technological capabilities, network and security service delivery experience, and compliance for global deployments.
Opportunities and Challenges for Organizations
Adopting and integrating AI into enterprise operations presents new opportunities and challenges. Here we see two major aspects: securing AI deployments and AI for networking and security.
- Securing AI Deployments: Ensuring secure and performant AI workloads and GenAI applications involves robust access control, threat protection, and intellectual property protection.
- Using AI for Networking and Security: AI opens vast possibilities for companies, provided they have the network bandwidth to handle massive data transfers. A reliable network is crucial for unlocking AI benefits and enforcing robust security controls.
AI’s Impact on Networking and Security – and Observability
As mentioned, data is a critical factor for both. Enterprises require enhanced network bandwidth with capabilities like data compression and deduplication to active AI benefits. LLMs and Retrieval-Augmented Generation (RAG) models utilize massive data transfers over networks for model deployment, data feeding, fine-tuning, updates, and model synchronization. The burden falls on the network and when capacity falls short, it can harm the core functions of the business. Securing AI deployments is the need of the hour to reduce the AI-centric attack surface by focusing on:
* Access Control: Implement robust authentication and authorization mechanisms to prevent unauthorized access and misuse.
* Threat Protection: Defend against external threats like data poisoning and prompt injection attacks.
* Intellectual Property Protection: Knowledge Leakage Prevention (KLP), in addition to Data Leakage Prevention (DLP), is essential to safeguard proprietary knowledge within AI applications.
Observability is critical to optimizing AI in networking and security. Staff and vendors must have tools to observe traffic comprehensively, identifying which AI tools and services are used, by whom, and why.
What to Look Out for to Maximize AI Benefits
Mitigating bias in AI models can reduce errors, such as false positives and false negatives, to ensure desired outcomes. Balancing automation and human oversight are critical to prevent AI hallucinations. AI services—and staff—must continuously learn and adapt to stay effective against new threats. Deploying AI requires significant investment, and enterprises should conduct cost-benefit analyses to modernize, optimize, and transform their network and security infrastructure and choose vendors that offer scalable services.
In conclusion, AI in networking and security can revolutionize enterprise operations, offering enhanced efficiency and security. However, success requires robust data hygiene, scalable infrastructure, continuous skill development, and stringent privacy measures. By investing in secure, adaptable solutions, like unified SASE as a service organization can harness AI’s full potential while safeguarding against emerging threats, positioning themselves for future success in an increasingly digital world.
Shailesh Shukla is the CEO and Chair of the Board of Directors at Aryaka.