To be successful, today’s businesses need more from their networks. They need intelligent, automated, secure networking that’s flexible enough to easily accommodate change, like launching new product lines, opening new sites, or introducing new technology. Why aren’t today’s networks living up to these demands? And how does agentic AI promise to change the way that networking and security operate?
In our last blog, we looked at what agentic AI is, how it works, and why it has the potential to change the way we do business.
Before we explore what agentic AI means for networking and security, let’s look at the shortcomings of today’s operational models.
Networking Tools Don’t Play Well With Others
Enterprise networks are made up of many different systems, featuring tools that often work in isolation from each other.
There are tools for BSS/OSS, networking monitoring and orchestration, ticketing, and billing — and that’s just a few of them. If an organization’s network is made up of services from different carriers across different geographies, that complexity is multiplied.
Many useful AI features and functions are being introduced into these tools. But their inclusion doesn’t solve the problem that most of these tools don’t integrate well, which means:
- There are blind spots in the network
- Securing the network is harder, because you can’t see all of it
- Network management ends up being more reactive and focuses on patching issues
- It takes longer to fix problems, which affects performance and increases the risk of downtime
- Network operations and security operations are separate and have no shared visibility, so vulnerabilities are increased and overlapping issues are more difficult to resolve
- Predicting the potential business impact of security and network events is trickier
- It’s harder to keep track of where confidential data is moving and keep it secure at all times
Agentic AI Cuts Through Network Complexity
As we’ve seen, agentic AI involves taking individual AI agents and connecting them into a single autonomous system. What happens when we apply this model to networking and security?
A workflow in networking might call on many different systems and related tools to achieve the desired outcome. If we plug an AI agent into each of these and combine them into a single agentic AI system, these workflows can be intelligently automated.
Let’s take a simple example. If there’s a problem with an organization’s network, an automated agentic AI system could work through a series of actions, such as:
- Checking billing systems to make sure the disruption isn’t due to non-payment
- Pinging network gateways
- Parsing interface statistics, like error rates and packet loss
- Checking ARP tables
This approach would allow the system to triage the problem without human intervention, and to identify the root cause. Depending on the issue identified, this information could then be passed to a member of the team for action, or to another AI agent to rectify.
For the first time, the agentic AI approach offers an operational model that unifies visibility, orchestration, management, monitoring, analytics, and reporting across the entire enterprise network estate — including both wireline and wireless.
Agentic AI’s comprehensive nature allows it to overcome the inherent complexity of enterprise networking — and all the challenges that come with it.
So, what are the wider implications of this approach?
Networking and Security Are Unified for Visibility and Resilience
Agentic AI can bridge the gap between network and security and bring them together into a unified solution in real time. This capability provides security and network teams with full visibility across the entire estate, reducing blind spots and vulnerabilities and minimizing the effect of security events.
Imagine, for example, that the Security Operations Center (SOC) opens an investigation into a security event. An agentic AI system correlates information on the potential impact on performance, availability, and user experience. It then informs the Network Operations Center (NOC) so that appropriate action can be taken to protect network performance. An AI agent could then either take action to mitigate the security event or pass relevant information onto a human to handle.
Agentic AI can also autonomously handle routine actions like monitoring policy and control adherence, making operations more efficient.
Identifying the Information That Matters Means Faster Problem Resolution
Network and security systems create vast amounts of noise, and it can be difficult for human teams to make sense of so much data.
Agentic AI has the capacity to ingest, analyze, and prioritize this data so that:
- Incipient issues can be identified before they impact the network or affect users
- IT staff can understand the wider context quickly
- Root causes can be pinpointed in minutes rather than hours
For example, our own AI-powered platform allows users to view the network at both a site level — including such information as network health and potential risk — and zoom in to a component level to quickly identify issues that might be affecting performance and uptime. As a result, customers experience an average 20% increase in the auto-closure of tickets and a 25% reduction in ticket volume.
Stronger Analytics Allow Proactive Decision-Making
By tying together disparate systems, reports, alerts, and other information, agentic AI can provide far more comprehensive analytics than humans realistically can alone — and it can do so much faster.
A simple example on our own platform is the Request for Outage (RFO) process. Instead of lengthy manual processes, our AI-powered platform extracts information from the ticket related to the event, summarizes it, creates a timeline, puts it into a templated document, and gets it ready for a human to review before it’s sent to the customer.
That means 90% of the work is already done by the time a human gets involved, significantly shortening the time it takes for us to respond to customer requests.
Improving visibility and analytics allows network and security teams to operate less reactively and more strategically, and to make better-informed decisions. For example, agentic AI systems can predict capacity demands with 95% accuracy up to 30 days in advance, so these needs can be addressed before high traffic and bottlenecks impact users.
Data Security Rules Can Be Enforced
Agentic AI can also help to improve data security by intelligently applying business rules to requests that span multiple different systems.
For example, if a request comes into the agentic AI system, it can interpret whether there is any CUI (classified user information), PII (personal information), or any other sensitive data.
The system can then apply business rules to make sure that this kind of data is kept within a secure enclave, and isn’t exfiltrated to other tools or public systems.
At a Glance: The Benefits of Agentic AI for Networking and Security
- Overcomes complexity and provides holistic visibility
- Unifies networking and security
- Speeds up issue resolution
- Allows more proactive network and security management to boost performance and resilience
- Cuts through noise and provides better insight for decision-making
- Saves employee time and increases productivity by automating routine tasks
Bringing Networking and Security Closer to the Business
It’s still vital that agentic AI has human oversight, particularly for issues of data security and governance. However, the automation, visibility, control, and performance that agentic AI promises will profoundly change the way networking and security operate — and will ultimately make it possible to align enterprise infrastructure with business outcomes much more closely. As organizations increasingly struggle to cope with change, that alignment could mean the difference between surviving and thriving.