How AI and Machine Learning Tools work for you
The open-ended nature of machine learning permits a nearly limitless application of an engine and its machine learning abilities. In addition to operating successfully with any API, it is hardware and network agnostic. The ability to automate and analyze your system operations is nearly unlimited.
Here are a few common use cases.
Use case: Security
One of the most straight-forward applications of machine learning is in Network Security. It can also be the one of the most powerful and useful applications. The key to a successful network security posture is to respond quickly to serious issues without letting the mundane or routine issues interrupt normal operations.
Existing network security applications monitor and report back on connectivity, protocols and access. When attempting to assure that a particularly vulnerability is not compromised, there is a tendency to lean towards over-reporting in the interests of ensuring that you’ve covered all potential access points.
The engine can respond and examine any routine alert and analyze the root cause
The result can be a flood of alerts that a person must manual examine and dismiss as a routine part of their job. These tickets may represent a legitimate threat or simply be a minor issue that’s impacting network performance. In theory, the IT staff should document the problem and the solution and use that information to remedy the issue. The reality is that most IT departments are too busy chasing down unusual tickets and don’t have the time to properly document the issue and the solution to minor problems.
An AI engine can help alleviate this workload in two ways. The engine can respond and examine any routine alert and analyze the root cause. If appropriate, it would close and dismiss the ticket as routine traffic. Most important, an AI can log the occurrence with full documentation of all current conditions before and after the fix for later use in analysis or reporting. This documentation of a routine solution can be as extensive as needed and can help inform a complete solution at a later date.
If the engine encounters something unusual or non-routine, it will escalate the ticket as appropriate to a person or it may even take automated action to secure the network from the possible threat while awaiting a response.
The second way AI can contribute to your overall network security is through the use of reporting and analysis across multiple platforms and channels. Even if your security policy still requires a human response to particular routine trouble tickets, the engine can handle all the investigation, reporting and “paperwork” related to the ticket. This turns a series of routine tickets into useful and complete data points for trend analysis.
Using AI to monitor network security frees up man-hours and expertise to focus on other issues.
Use Case: Business Process Automation for Network Monitoring
Closely related to the security function is the monitoring and maintenance of system performance. An AI machine learning tools is able to ingest and analyze information on patches, system updates and other data streams and compare them to each machine on your network. This ensures that each component in your system is operating on the correct version of any application or OS.
It can also analyze logs from startup or applications to detect and mitigate slowdowns, disk-space issues, network ping times or other contributors to decreased system performance.
By taking advantage of the structured and unstructured data already generated in your network infrastructure, the engine can ensure each component is operating consistently at peak efficiency.
Example: In a network that supports 4,000 local Windows devices, the AI engine is able to scan and monitor performance. It quickly determines average boot times, memory and CPU usage and other factors that these similar devices have in common – even accounting for differences in hardware and installed software. After creating a framework for an average functioning machine, it can compare live metrics on each machine to assess individual performance. When one machine starts to boot a little more slowly or hang on certain processes, it can either recommend a maintenance check or — if allowed by your policy — even run its own analysis and repairs on the system.
Example two: While monitoring the individual devices, the system can learn usage patterns and analyze application activity against licensing. Such a company-wide audit may show that certain machines have an ongoing license for an expensive application that is never used. When aggregated, this data can save a company thousands in reduced licensing costs.
By aggregating system monitoring and examining it from a machine-learning perspective, the engine is able to act as an intelligent triage for any large-scale issues. If a system-wide event breaks network connectivity for a large segment of your devices, monitoring tools on every single system are programmed to alert immediately to make sure human eyes on the case as quickly as possible. Because every device that went offline alerted at the same time, there is a flood of tickets and notifications throughout the system.
In a typical IT department, a person has to examine each ticket and drill down to determine the device affected, the exact problem it’s having and the root cause. That’s a ton of clicking, typing and waiting just to process the initial flow. With automation in place, the tickets are automatically processed, compared and grouped into a single alert listing all the machines with the same reported problem. This takes the initial stage of analysis out of human hands and lets IT techs start a real root analysis on the big problems.
Use Case: Data Analysis
Building frameworks to deal with Big Data problems is an ongoing challenge. The ability to use AI to work with Big Data is a synthesis of proven methodologies and database interactions.
AI assists with much of arduous labor of combining data from unrelated sources, delivering a more coherent look at the information you collect. By bringing together everything you have under one roof, the real work of analysis and true deep understanding can take place.
Use Case: Marketing Analysis
For many, taking a AI machine learning tool out of the back IT office and putting it in the hands of sales or marketing people might seem like an edge case. But marketing and sales data is closer to logistics or operational data than many people think.
Successfully drawing analytics-based conclusions will help you launch the next stage of an ai-powered marketing playbook
When examined in the right way, marketing response is simply another revenue channel that has measurable metrics and data streams.
Using its ability to interface with an API, an AI engine can lock in with Enterprise Resource Planning software to examine your key performance indicators. Just one example would be an analysis of the precision and profitability of delivering messaging through targeted social media. Gathering and compiling information beyond a simple click-through report can be a tedious and difficult manual task. But when it is properly integrated with the infrastructure, this information flows down easily into a report, a meaningful metric or even a dashboard style “pane of glass” that gives a complete look at all revenue channels.
Gathering that information and successfully drawing analytics-based conclusions will help you launch the next stage of an AI-powered marketing playbook. Once you know which channels and methodologies are successful to each slice of the market, the engine can help automate content delivery in the form of contextual content, personalization and targeted cross-channel campaigns.
The flexibility and growth potential is nearly unlimited in this area, and many experts believe the use of machine learning in marketing is only scratching the surface.