Tines today added an artificial intelligence (AI) chat interface to its no-code platform for automation cybersecurity workflows.

Company CEO Eoin Hinchy said Tines Workbench makes use of multiple large language models (LLMs) to make it simpler for cybersecurity teams to orchestrate a range of tasks using a natural language interface.

Tines previously enabled cybersecurity teams with little to no programming expertise to employ a platform that surfaced a graphical user interface (GUI) through which tasks could be automated in real-time. The addition of Workbench further democratizes security automation by making it simpler to query, gather and analyze.

At the same time, Tines Workbench includes built-in guardrails to avoid hallucinations and reduce the likelihood that misleading answers will in response to, for example, summarization requests could be generated, noted Hinchy

The overall goal is to make it simpler for cybersecurity teams to derive more value from the plethora of tools they already have invested in to achieve and maintain cybersecurity without having to become prompt engineers, he added.

In general, cybersecurity workflow automation has generally been applied over the years unevenly. One of the main challenges is the level of programming expertise that has been required by many platforms. Most cybersecurity teams don’t have a lot of programming skills on hand. Tines has addressed that issue using a set of intuitive graphical tools that invoke a no-code automation engine it developed.

Generative AI now provides an alternative interface that should make the core Tines automation framework even more accessible, said Hinchy.

In theory, that capability should make it simpler for IT operations teams to, for example, understand what they might need to specifically fix to resolve a cybersecurity issue.

Given the ongoing chronic shortage of cybersecurity professionals, the only way to effectively close that gap is to rely more on automation. In theory, artificial intelligence (AI) will soon make it easier to identify cyberattacks, but there still needs to be a framework for automating the deployment of the controls required to thwart those attacks. The challenge cybersecurity teams invariably encounter is the automation frameworks they might have previously employed are often too complex. No one on the cybersecurity team has the time required to master them.

One way or another, cybersecurity needs to become a lot more automated. Time, after all, is always of the essence when there is a cyberattack. The ability to limit the blast radius of a cyberattack depends on how quickly cybersecurity teams can respond. Most cybersecurity teams are now measured as much by their ability to respond and contain an incident as much as they are by the number of threats they thwarted in the first place.

Of course, it’s not always easy to justify investments in cybersecurity during difficult economic times. However, as the types of cyberattacks being launched continue to increase in volume and sophistication there is a clear need for everyone involved to collaboratively find a way to work smarter when every second counts.