One issue in cybersecurity, operational IT management and data management is the cost to solve issues, quickly, well, and cheaply. Fast, good, and cheap! Here’s a real life example.
One of my customers was subject to a ransomware attack, and they called for advice on whether to pay the hackers or engage with cybersecurity professionals. I told them I could not recommend engaging with criminals at all since it would be unethical to do so. I asked them why they were considering it. They told me that the cybersecurity professionals were charging the same amount of money as the hackers (!), and they would most likely get their data back more quickly if they just paid the hackers.
To be clear, I do not recommend engaging with criminals. However, I could see the customer perspective; humans always choose convenience. In technology, AI is becoming more popular because it is convenient.
Co-pilots: good, fast or cheap?
In the ever-evolving landscape of technology, AI, and user needs, the concept of a co-pilot has emerged as a valuable asset in streamlining and enhancing various processes, particularly in coding and technical environments. Companies like Microsoft, OpenAI, Commvault, and GitHub have been at the forefront of developing cutting-edge co-pilots that leverage state-of-the-art algorithms to enhance development capabilities. AI is all about data; faster data storage and retrieval is crucial for data-based environments, including the metaverse. AI analyzes data storage patterns and optimizes data placement across on-premises and cloud environments, potentially lowering costs and improving access speeds. AI can automate routine data management tasks, reduce human error, and free up IT staff for more strategic work. Another way of supporting security and compliance is through implementing AI algorithms to detect and respond to anomalies in data patterns, which could indicate a security breach or non-compliance with regulations.
What is a co-pilot?
In tech terminology, a co-pilot is an AI-powered or machine learning-based tool designed to assist and collaborate with users in a specific task, especially in coding and software development. They use advanced algorithms that analyze data, provide suggestions, offer solutions, and facilitate a more efficient and effective workflow. One of the most notable updates is integrating natural language processing (NLP) models in co-pilots with advanced ‘behind the scenes’ Artificial Intelligence, enabling them to understand and generate more effective human-like text, code snippets, and suggestions.
The primary purpose of a co-pilot in tech is to augment team members’ and developers’ capabilities by offering intelligent suggestions, automating repetitive tasks, and enhancing overall productivity. Co-pilots can provide code completions, documentation support, error detection, and even real-time collaboration, empowering programmers and operational teams to deliver in less time. Ultimately, the goal is to streamline the problem-solving process and optimize the delivery cycle.
Are co-pilots just another AI buzzword?
Co-pilots are becoming central to AI-savvy organisations. Initiatives such as Microsoft’s Security Co-Pilot and Commvault’s Arlie are innovations and advancements in AI and machine learning technologies to help with operational problem-solving. Commvault is known for its innovation and commitment to integrating cutting-edge technologies, including AI, to address complex challenges. Commvault’s Arlie uses AI and ML capabilities to expedite operational tasks, provide real-time insights, and protect against common human errors. Arlie can assist with reporting and root cause analysis seamlessly and intuitively, allowing users to access and leverage AI’s power effortlessly.
Humans love convenience, which is where co-pilots like Arlie come in. They cater to a wide range of users, from seasoned professionals seeking to boost their productivity to novice team members looking for guidance and support in coding tasks. By catering to experts and beginners, Arlie helps operational management happen more quickly and conveniently.
Commvault's Arlie
Commvault’s Arlie provides AI and ML capabilities to automate your operational tasks, so you can spend less time on them and more time on productive and creative activities.
Arlie is part of the Commvault cloud, and team members can interact with it from everywhere as it follows you around the interface no matter where you are. From a data perspective, AI is embedded functionality in the platform’s Data Fabric. The objective is to democratize the technical delivery process, whether development or troubleshooting, making it more accessible and efficient. AI assists team members with every action they take as they navigate through being productive throughout the work day. Arlie can save you time by acting on your behalf, allowing you to focus on other activities.
Examples of Arlie in action
Arlie can help with a wide range of tasks. For convenience, it can supply you with reports providing real-time daily digests of things that matter most to you, such as the organization’s most business-critical applications. For example, it will help you check how the organization’s virtualized environments perform. It helps you quickly identify and nail any failed Virtual Machine backups for any of your essential VMs from the past day. Further, we can ask Arlie why the backup jobs are failing on specific Virtual Machines. Arlie uses the data from the Data Fabric as a basis for the advanced algorithms to provide relevant, always-on root cause analysis to pinpoint the issue. Arlie then presents the team members with root cause analysis and recommendations to show why the Virtual Machine has failed, thereby helping the team see the next steps towards resolution. Arlie can also help the team members identify how widespread this issue is and whether any databases have a similar problem. Finally, Arlie could then email this report to relevant personnel.
Personal thoughts on Arlie
Seeing Arlie in action impressed me with how well it worked to get to the answers quickly. The issue with co-pilots is that they can often give quite general answers, and the user has to work hard to get the answer they want, like an IVR system that only allows you to get the answer you want. Since Arlie is using data in the Commvault cloud in a specific domain, it can achieve high accuracy and expedite troubleshooting. They utilize AI for predictive analytics, helping businesses anticipate data growth and requirements and make informed decisions about issues that reoccur in their environment.
In conclusion, co-pilots in tech represent a transformative force reshaping how technical teams code and collaborate. By harnessing the power of AI and machine learning, co-pilots like Arlie empower users to get to a good place more quickly while also fostering collaboration in software development. As the technology evolves, developers and stakeholders must stay informed about the latest trends and advancements in co-pilots to leverage their full potential and drive digital transformation in the tech industry.