cloud computing

Insight Engines, the San Francisco based startup focused on making machine data actionable, announced the general availability of Cyber Security Investigator and, also, showcased how Amazon Alexa can be tapped to query from Cyber Security Investigator. In this note, we will do an analysis on this announcement.

Market Overview

AI in enterprise is relatively new. Even though enterprises are slowly embracing machine learning and other AI models to dig deeper into their customer data and make business decisions, there is very little progress in using AI to take advantage of machine data. There are plenty of analytics solutions that helps Operations teams optimize their decision making process. But the market is still in infancy when it comes to using ML to automagically do operations or use AI technologies like NLP to develop a better user experience for the machine data. Imagine how DevOps can be done more optimally if developers or even other stakeholders like business users can take advantage of NLP to interact directly with machine data. These are just beginning and we can’t imagine what AI can do to autonomic computing with our current understanding of the landscape.

SWOT Analysis

Strengths

• Powerful NLP engine to handle machine data, add context and give customization options to users
• Starting with Splunk data (with an investment from Splunk, of course) gives them an easy on ramp to enterprise IT
• Their initial focus on Security will help them get the attention of IT decision makers
• Autopilot feature that provides a more pro-active approach to security might serve as model for future autonomic computing platforms

Weakness

• They are a startup pushing a newer technology in the enterprise market. The barrier to entry for startups in enterprise is high. However, their partnership with Splunk should help them
• They provide on-premise deployments which makes it difficult for the learning engine to continuously improve. But they are tapping into the anonymized meta data to help the learning engines learn from user behaviors. This will work well with modern enterprises but they may have trouble convincing enterprises in highly regulated verticals. It is not a weakness for Insight Engines alone but for any company trying to build AI systems that can be deployed on-premises. To overcome this, Insight Engines, as a pioneer in this space, has to convince the customers to share their metadata with them. It is a potential weakness but also an opportunity for them to emerge as thought leaders

Opportunities

• Insight Engines has the first mover advantage and they are attacking low hanging fruits (machine data and security) with a more powerful NLP engine. They can easily broaden their product portfolio going forward
• As we embrace modern stacks with deeper and deeper levels of automation, ML and AI are going to be the next wave of innovation. The early innovations in this kind of autonomic IT will come around user interface and user experience. Insight Engines is well positioned to take advantage of the trend

Threats

• Many established players collect lot of machine data (including Splunk) and it is a logical next step for them to attack the low hanging fruits like NLP for UI/UX. Though it is a threat, it is also an opportunity
• Open source NLP engines can come and disrupt the market. OSS need not come from the traditional IT companies but also from end customers who develop ML and AI engines for their internal use. There is also an opportunity for Insight Engines to lead here but OSS by startups is not easy.

Conclusion

Insight Engines is an interesting startup in the up and coming field of AI in enterprise IT. It is too early in the market but offers potential opportunities for enterprises to do IT more optimally and optimize the use of human power in house by involving more stakeholders and by reducing the learning curve.