Putting AI Into AIOps: A Future Beyond Dashboards

In today’s fast-paced IT environment, traditional dashboards and reactive alert systems are quickly becoming outdated. The digital landscape requires a more proactive and intelligent approach to IT operations. Enter Artificial Intelligence (AI) in IT Operations (AIOps), a transformative approach that leverages AI to turn data into actionable insights, automated responses, and enabling self-healing systems. This shift isn’t just integrating AI into existing frameworks; it has the potential to fundamentally transform IT operations.

The Evolution of IT Operations: From Reactive to Proactive

The traditional model of IT operations has long been centered around dashboards, manual interventions, and reactive processes. What once sufficed in simpler systems is now inadequate in today’s complex, interconnected environments. Today’s systems produce vast data of logs, metrics, events, and alerts, creating overwhelming noise that hides critical issues. It’s like searching for a whisper in a roaring crowd. The main challenge isn’t the lack of data, but the difficulty in extracting timely, actionable insights.

AIOps steps in by addressing this very challenge, offering a path to shift from reactive incident management to proactive operational intelligence. The introduction of a robust AIOps maturity model allows organizations to progress from basic automation and predictive analytics to advanced AI techniques, such as generative and multimodal AI. This evolution allows IT operations to become insight-driven, continuously improving, and ultimately self-sustaining. What if your car could not only drive itself and learn from every trip, but also only alert you when critical action was needed, cutting through the noise and allowing you to focus solely on the most important decisions?

Leveraging LLMs to Augment Operations

A key advancement in AIOps is the integration of Large Language Models (LLMs) to support IT teams. LLMs process and respond in natural language to enhance decision-making by offering troubleshooting suggestions, identifying root causes, and proposing next steps, seamlessly collaborating with the human operators.

When problems occur in IT operations, teams often lose crucial time manually sifting through logs, metrics, and alerts to diagnose the problem. It’s like searching for a needle in a haystack; we waste valuable time digging through endless data before we can even begin solving the real issue. With LLMs integrated into the AIOps platform, the system can instantly analyze large volumes of unstructured data, such as incident reports and historical logs, and suggest the most probable root causes. LLMs can quickly recommend the right service group for an issue using context and past incident data, speeding up ticket assignment and resulting in quicker user resolution.

LLMs can also offer recommended next steps for remediation based on best practices and past incidents, speeding up resolution and helping less experienced team members make informed decisions, boosting overall team competence. It’s like having a seasoned mentor by your side, guiding you with expert advice for every step. Even beginners can quickly solve problems with confidence, improving the whole team’s performance.

Revolutionizing Incident Management in Global Finance Use Case

In the global finance industry, seamless IT operations are essential for ensuring reliable and secure financial transactions. System downtimes or failures can lead to major financial losses, regulatory fines, and damaged customer trust. Traditionally, IT teams used a mix of monitoring tools and manual analysis to address issues, but this often causes delays, missed alerts, and a backlog of unresolved incidents. It’s like managing a train network with outdated signals as everything slows down to avoid mistakes, but delays still lead to costly problems. Similarly, traditional IT incident management in finance slows responses, risking system failures and trust.

IT Operations Challenge

A major global financial institution is struggling with frequent system outages and transaction delays. Its traditional operations model relies on multiple monitoring tools and dashboards, causing slow response times, a high Mean Time to Repair (MTTR), and an overwhelming number of false alerts that burden the operations team. The institution urgently needs a solution that can detect and diagnose issues more quickly while also predicting and preventing problems before they disrupt financial transactions.

AIOps Implementation

The institution implements an AIOps platform that consolidates data from multiple sources, such as transaction logs, network metrics, events, and configuration management databases (CMDBs). Using machine learning, the platform establishes a baseline for normal system behavior and applies advanced techniques like temporal proximity filtering and collaborative filtering to detect anomalies. These anomalies, which would typically be lost in the overwhelming data noise, are then correlated through association models to accurately identify the root causes of issues, streamlining the detection and diagnosis process.

To enhance incident management, the AIOps platform integrates a Large Language Model (LLM) to strengthen the operations team’s capabilities. When a transaction delay occurs, the LLM quickly analyzes unstructured data from historical logs and recent incident reports to identify likely causes, such as a recent network configuration change or a database performance issue. Based on patterns from similar incidents, it determines which service group should take ownership, streamlining ticket assignment and accelerating issue resolution, ultimately reducing Mean Time to Repair (MTTR).

Results

• Reduced MTTR and MTTA: The financial institution experiences a significant reduction in Mean Time to Repair (MTTR) and Mean Time to Acknowledge (MTTA), as issues are identified and addressed much faster with AIOps. The LLM-driven insights allow the operations team to bypass initial diagnostic steps, leading directly to effective resolutions.
• Proactive Issue Prevention: By leveraging predictive analytics, the platform can forecast potential issues, allowing the institution to take preventive measures. For example, if a trend suggests a potential future system bottleneck, the platform can automatically reroute transactions or notify the operations team to perform preemptive maintenance.
• Enhanced Workforce Efficiency: The integration of LLMs into the AIOps platform enhances the efficiency and decision-making capabilities of the operations team. By providing dynamic suggestions and troubleshooting steps, LLMs empower even the less experienced team members to handle complex incidents with confidence, improving the user experience.
• Reduced Alert Fatigue: LLMs help filter out false positives and irrelevant alerts, reducing the burden of noise that overwhelms the operations team. By focusing attention on critical issues, the team can work more effectively without being bogged down by unnecessary alerts.
• Improved Decision-Making: With access to data-driven insights and recommendations, the operations team can make more informed decisions. LLMs analyze vast amounts of data, drawing on historical patterns to offer guidance that would be difficult to obtain manually.
• Scalability: As the financial institution grows, AIOps and LLMs scale seamlessly, handling increasing data volumes and complexity without sacrificing performance. This ensures that the platform remains effective as operations expand.

Moving Past Incident Management

The use case shows how AIOps, enhanced by LLMs, can revolutionize incident management in finance, but its potential applies across industries. With a strong maturity model, organizations can achieve excellence in monitoring, security, and compliance. Supervised learning optimizes anomaly detection and reduces false positives, while generative AI and LLMs analyze unstructured data, offering deeper insights and advanced automation.

By focusing on high-impact areas such as reducing resolution times and automating tasks, businesses can rapidly gain value from AIOps. The aim is to build a fully autonomous IT environment that self-heals, evolves, and adapts to new challenges in real time much like a car that not only drives itself but learns from each trip, optimizing performance and solving issues before they arise.

Conclusion

“Putting AI into AIOps” isn’t just a catchy phrase – it’s a call to action for the future of IT operations. In a world where the pace of change is relentless, merely keeping up or treading water isn’t enough; Organizations must leap ahead to become proactive. AIOps is the key, transforming vast data into actionable insights and moving beyond traditional dashboards.

This isn’t about minor improvements, it’s a fundamental shift. Imagine a world where issues are predicted and resolved before they cause disruption, where AI helps your team make smarter, faster decisions, and operational excellence becomes standard. The global finance example shows real benefits; reduced risks, lower costs, and a seamless user experience.

Those who embrace AI-driven AIOps will lead the way, redefining success in the digital era. The era of intelligent, AI-powered operations is here. Are you ready to lead the charge?

Source: cisco.com

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AIOps Drives Exceptional Digital Experience Through Network Assurance

The distributed workforce―and the distributed applications and services they consume―have vastly changed the enterprise network paradigm. Many connections—such as private cloud, internet, public cloud, multicloud, and software-as-a-service (SaaS) networks—now begin and end outside of the traditional corporate infrastructure. The coexistence of these complex connections creates new layers of operational complexity for teams responsible for ensuring predictable performance and quality of service.

What is needed to combat this complexity is a network assurance platform that includes true end-to-end visibility capabilities. Insight is needed into users and their devices, locations, and connected things, as well as into access networks, network services, multiple clouds, and corporate enterprise data centers and applications (Figure 1). A solution that combines these different data sets and uses artificial intelligence and machine learning (AI/ML) to analyze the data, can help drive decisions that make network operations proactive and predictive, instead of reactive.

Figure 1. Span of end-to-end visibility required (click to enlarge)

In our 2023 Global Networking Trends Report, nearly half (47%) of respondents said they are prioritizing the adoption of predictive network analytics over the next two years, primarily to help with managing the connectivity and digital experience of their remote workforce.

A predictive network analytics solution requires the ability to correlate massive amounts of network data in real time and at tremendous scale. By continuously analyzing performance data and applying predictive modeling to forecast conditions and recommend actions, predictive capabilities can become a reality. Predictive analytics empowers teams to avoid adverse application impacts to distributed workers and to ensure the best possible user experience.

Predictive analytics for SD-WAN and an internet-centric world

For the software-defined WAN (SD-WAN), a platform that uses artificial intelligence for IT operations (AIOps) can provide predictive analytics to forecast performance (Figure 2). AIOps refers to the strategic use of AI, ML, and machine reasoning (MR) technologies to simplify and streamline IT processes and optimize the use of IT resources. By correlating and analyzing real-time and historical SD-WAN performance data and applying predictive models, AIOps can use these forecasts to deliver per-site recommendations for optimal path selection by application type to deliver an optimal experience based on available paths.

By integrating predictive analytics into SD-WAN solutions, IT teams can improve dynamic enforcement of application service levels with intelligent routing across alternative paths before any degradation occurs.

Figure 2. Predictive analytics through a continual feedback loop (click to enlarge)

Combining traffic data sets from an organization’s ecosystem of ISPs, cloud providers, SaaS applications, and other external services, further enriches predictive analytical systems. Operations teams can rapidly identify, escalate, and remediate issues with providers using internet telemetry data. When outage behavior is detected, a root cause can be identified and shared with providers to prioritize fixes or escalate to peers and transit providers.

Predictive analytics at work in the real world 

When Insight Global—one of the largest staffing agencies in the United States—allowed its employees to return to the office, they leveraged information from ThousandEyes’ WAN Insights to optimize its SD-WAN policies and improve application experiences proactively and continuously. Once the solution was in place, they gained greater visibility into critical network environments and routing, and Insight Global’s IT team was better able to detect and avoid potential issues before those issues could impact the business.

Predictive and proactive operations is the way forward

It’s time to move from reactive to proactive operations management through end-to-end visibility and AI/ML-powered predictive analytics. It’s time for a consistent way of automating operations, analyzing and diagnosing issues, and assuring the user experience across all the different networking domains.

We believe strongly in this way forward. It’s the cornerstone of Cisco’s approach to network assurance and Cisco’s Networking Cloud vision—a unified management experience platform for on-premises and cloud operating models to simplify IT, everywhere, at scale.

Source: cisco.com

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Innovation at the inner core of Cisco DNA Center

Cisco DNA Center has seen several releases with significant innovation and the evolution of the product platform. With DNA Center capabilities aligned to Gartner’s four IT personas (AIOps, NetOps, SecOps, and DevOps), it is important to take a step back and look at the platform or networks-put the “underlay.”

With changes in the IT landscape, several megatrends are shaping what the network platform needs to deliver. With the new landscape where both applications and users are on the move, the face of the campus network has changed and expanded.

Figure 1. Megatrends shaping digital transformation

Cisco DNA Center Virtual Appliance, deployment flexibility

With applications moving to the cloud, it is no surprise that management platforms are moving to the cloud. Cisco DNA center is no exception. DNA Center is now able to run on AWS, and the deployment of the AWS VA takes under an hour from start to finish.   A lot of flexibility is also provided to the end user through the support of a launchpad to automate the installation or through a manual mode for users who already have a custom AWS environment. DNAC install is completely programmatic in both cases (no login to shell required!)

At this point, users can get on the Cisco DNA Center UI and begin configuration, discovery, and more.

Figure 2. Virtual Appliance Diagram

Following AWS, a VMWare version of the appliance will be released, allowing customers to use their existing VMWare infrastructure to run Cisco DNA Center instead of a physical appliance. As part of Cisco’s commitment to the platform, no matter how you deploy Cisco DNA Center, users will see feature parity it is the same Cisco DNA Center code and capabilities.

Hardened Security Features

Some verticals, industries, and organizations have specific security requirements mandated, such as FIPS.

Activation of FIPS compliance at Cisco DNA Center install time enables security features such as secure boot, TPM, session timeouts, and password expiration.  When data is shared using weak or deprecated ciphers, that data is at risk of being decrypted by malicious actors. Cisco DNA Center now supports FIPS 140-2-compliant cryptography modules, ensuring that only strong NIST-approved ciphers are used and enabling deployment in security-conscious verticals such as the public sector, finance, and healthcare.

Figure 3. FIPS compliance letter

ACL to management access for Cisco DNA Center appliance

By popular demand – many customers utilize ACL’s to control access to the network devices for management. As Cisco DNA Center is now the centralized monitoring and management point for network estate, customers can now create ACL’s to control what networks or IPs can access the Cisco DNA Center UI

Restricted shell support

Again by popular demand customers have requested to provide an enable shell for DNA Center so that sensitive CLI commands can be protected at all times. DNAC now comes with a restricted shell as standard and only non-invasive CLI is allowed to be run on the console. For any  CLI which requires root level / Sudo permissions, the shell will default deny it. A special token needs to be acquired to remove the restriction.

Scale – the agility to keep up with your business

Scale is a constant growth factor with post-pandemic life coming back to normal, with the proliferation of IoT and OT devices on the network on the rise. There is a constant need to ensure that the network management and orchestration platform can continue to scale with the network and business needs. With each release, Cisco DNA Center team has been making continuous strides with an increased platform scale. Recent scale updates for version 2.3.3 include up to 6,000 sites and 24,000 devices (Access Points and Network Devices for both Fabric and non-fabric networks).

Figure 4. DNA Scale

Remote support

As part of improving the support engagement between customers and TAC, at times providing TAC easy access to the equipment has contributed to extending the MTTR (Mean time to repair). To ease the process, customers are now able to allow TAC access to network equipment via Cisco DNA Center. This solution enables the customer to provide TAC-specific access to equipment and the ability to revoke access at any time.

Figure 5. Remote Support Activation

Aura (Audit & Upgrade Readiness Analyzer)

AURA stands for Audit & Upgrade Readiness Analyzer and performs various health, scale, and upgrade readiness checks for the Cisco DNA Center and the rest of the Fabric network. The tool is extremely simple to run and is executed on the Cisco DNA Center.

Figure 6. AURA screen image

The tool uses API calls, DB reads and CLI show commands (read-only operations) and hence, doesn’t affect performance or cause impact the Cisco DNA Center or the networking devices. This functionality was built in collaboration with Cisco DNA Center Escalation Engineering, Sales, and CX Centers TAC Engines team to ensure an efficient upgrade experience. AURA Tool Check Areas:

◉ DNA Center Scale Test

◉ DNA Center Infra Health

◉ DNA Center Assurance Health

◉ WLC/eWLC Assurance Health

◉ SDA Device CLI Capture

◉ SDA Control & Security Audit

◉ Software Bugs Causing Upgrade Failures

◉ Upgrade Readiness Checks

◉ SDA Compatibility Check (Switches, Wireless Controllers & ISE for 2.2.2.x)

◉ DNAC-ISE Integration Checks

◉ Fabric Devices Configurations Capture and Compare using inbuilt diff tool

Figure 7. System Analyzer screen image

Source: cisco.com

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Empowering the four IT personas using Cisco DNA Center with Rings of Power

There are many variations of the “Law of Constant Change”; while they all have their own spin on it, the common thread is that change is constant and that it needs to be harnessed. When looking at changes and disruptions in technology, it comes as no surprise that there are numerous transformations and trends which are reshaping the IT landscape. The megatrends and change drivers span a wide range of business changes and transformation agents such as:

To keep up with the rapidly changing IT landscape, many IT organizations have been able to ascend and transform into new operational paradigms with the xOps transformation. Conversations around agility, AIOps, NetOps, SecOps, and DevOps are an outcome of a combination of organizational behavior and tooling in the networking and infrastructure realms. Separately, Gartner has also identified four IT personas (NetOps, SecOps, AIOps, and DevOps) which Gartner defined as predominant roles in today’s network operations realm.

In looking at key challenges, organizations are struggling with:

◉ Reducing time recovery objectives due to the reactive nature of traditional network operations practices.

◉ Bridging the growing IT skill gap.

◉ Keeping up with changing business requirements.

◉ Delivery of secure services in the hybrid workplace.

◉ Having to deliver more with less.

With Cisco’s years of expertise in designing, operating, and supporting networks of all sizes across the globe. Cisco has been an instrumental part in helping IT organizations move forward to the next operational level with tools to embrace and enable the xOps personas and embark on the transformation journey. This boils down to providing tools with analytics capabilities from the infrastructure and cultivating staff skills to use them effectively.

Speaking of how tooling can enable the transition, Cisco DNA Center is at the center of the IT/OT transition into the four IT personas, providing the digital agility to drive network insight automation and security while promoting key capabilities and tools to help in skill cultivation and changed operational models.

Network Operations or “NetOps” is the front line of administrators in the IT organization. The term NetOps is a way to classify the common tasks and responsibilities, or “Jobs to be Done,” by these individuals. With Cisco DNA Center at the heart of the network infrastructure, the NetOps persona is enhanced with varying levels of automation to simplify the creation and maintenance of networks with agile flexibility to move from manual tasks to AI-assisted to selectively autonomous network management. For example, the SWIM (Software Image Management) and network profiles feature not only save time but allow for consistency and elimination of human error with routine tasks. The NetOps automation brought into DevOps provides agility and scalability to IT organizations to keep up with changing demands and integration into the larger IT ecosystem. Gartner has stated that the next generation of Netops, which Gartner coined as “Netops 2.0” is the evolution of network operations towards automation.

Network, application, and user security is a key requirement for any enterprise network, and no network can operate safely without security. The security team is responsible for providing a safe digital experience in today’s connect-from-anywhere hybrid work environment and networks with countless numbers of endpoint devices. Also, many IT organizations in different market segments have various network security and architecture recruitments. Cisco DNA Center empowers the SecOps persona by enabling the complete zero-trust workplace solution with AI-driven security to classify endpoints and automated enforcement of security policies. This is achieved with Cisco’s fully integrated platform, which incorporates hardware and software designed to provide contextual security insights and automation. Cisco DNA Center SecOps can help eliminate security vulnerabilities with proactive security scans, automated security advisory alerting Cisco’s Product Security Incident Response Team (PSIRT), and proactive bug scans powered by Cisco AI Network Analytics engine to ensure the network is always secure.

The DevOps persona brings integration, automation, and orchestration together. Traditionally, DevOps teams focused on very specialized, proprietary, and home-spun applications. Today, these individuals are tasked with taking these apps and integrating them into a connected universe of corporate solutions. DevOps depends on manufacturer-supplied software tool kits (STKs) and standards-based application programming interfaces (APIs) in order to share information and intelligence between applications. With Cisco DNA Center, IT organizations can quickly utilize pre-built integrations to Cisco products and 3rd party enterprise applications such as ServiceNow, Splunk, PagerDuty, and a growing selection of partner integrations. Cisco DNA Center’s mature APIs enable the extraction of data and network management, leveraging and harnessing the power of Cisco DNA Center’s NetOps, AIOps and SecOps via the API interface.

AIOps defines the technologies that implement AI/ML (Artificial Intelligence and Machine Learning) and the individuals that leverage these technologies. Evidently, AI/ML is being implemented in so many of our networking components that it has become imperative that a specialized team of experts manage and amplify the use of this intelligence. Cisco DNA Center provides a simplified view into the complexities of big data and machine learning so that your AIOps teams can make the most of this rich data.  Additionally, Cisco DNA Center provides best-in-class AI-driven visibility, observability, and insights, ensuring the health and experience of users, applications, and infrastructure. AI/ML is packaged within Cisco DNA Center in an easy consumption interface that can deliver value in minutes and allow IT teams to work smarter and elevate the level of service to the users and organization. Hence, with Cisco DNA Center AIOps, IT organizations can gain visibility and insights otherwise not attainable without AI/ML combined with Cisco’s deep networking knowledge. Simply put, this powerful combination makes the IT team more agile and smarter and helps bridge growing IT skills gaps.

The xOps Rings of power

While the four IT personas were explained as distinct roles, in many organizations, they are simply different hats that IT staff can wear at different times depending on the business need.  It is also essential to keep the perspective that each of the personas enables and provides services to other personas, yielding the “Rings of Power” for example, with AI centricity, Cisco DNA Center empowers, enables, and enhances the NetOps, SecOps, and DevOps personas by providing interactions with all personas in the ring. Similarly, NetOps persona-centricity enables and empowers DevOps, SecOps, and AIOps personas.

An example of the AIOps ring of power:

AIOps discovers security vulnerabilities and recommends an upgrade.

NetOps performs the SWIM process to upgrade the software.

DevOps connects to ServiceNow for the change management and ticket creation processes.

SecOps reports the new network security posture, eliminating the security vulnerability from the network.

Leveraging Cisco DNA Center to enable and empower the new IT personas model, IT organizations can quickly and easily gain visibility, observability, insights, and out-of-the-box automation. While organizations with more modern operational models are also able to yield zero trust, and programmability from the Cisco Network infrastructure. This enables IT organizations to be more agile and transform into the new xOps operational paradigm, allowing the IT organization to progress on the operational maturity journey, become proactive and leave the reactive persona behind.

Source: cisco.com

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