7 Ways Security Teams Can Save Time With AI

AI has already revolutionized the way we work. ChatGPT, GitHub Copilot, and Zendesk AI are just a few of the tools that are taking over day-to-day tasks like generating customer support emails, de-bugging code, and much, much more. Yet despite all of these advancements, security teams are under more intense pressure than ever to mitigate rapidly evolving risks. Paired with a growing shortage of over 3.4 million cybersecurity workers, security teams are in need of a solution—and fast.

In this case, that solution also happens to be—you guessed it—AI. A recent IBM report uncovered that 28% of organizations have already begun using AI to enhance their cybersecurity efforts. AI helps security teams to respond to data breaches over 30% faster, while saving an average of $1.8 million in the process. But these are big-picture estimates; let’s zoom in a little bit to see exactly how AI is helping security teams to streamline their workflows on a day-to-day basis.

Installation

Cloud-based, AI-powered DLP solutions like Nightfall make an immediate impact on security team productivity, starting at installation. But what does being “Cloud-based” and “AI-powered,” have to do with security team productivity?

• Cloud-based DLP: Compared to traditional DLP, like network DLP and endpoint DLP, cloud-based solutions take only a fraction of the time to install. (Think minutes—not months.) In Nightfall’s case, this is because installation happens at the API level. That means no agents, no network, and no endpoint devices required.
• AI-powered detection: Once Nightfall is installed and ready to go, users can instantly deploy over 60 different ML-powered detectors right out of the box. These pre-trained detectors not only save countless hours of configuration time, but also give security teams a leg up if they want to build their own detectors using Nightfall’s regex library and SDKs.

Detection

Now let’s take a closer look at Nightfall in action. There are two ways that Nightfall’s AI can help security teams cut the operational costs of monitoring for violations. In the spirit of saving time, here are the two ways at a glance:

• Low signal-to-noise ratio: AI detection is significantly more accurate than regex detection because it uses neural network embeddings to consider the context surrounding a violation. This context helps detectors to assign “Confidence levels” of “Possible,” “Likely,” or “Very likely” to violations in order to cut down on false positive alerts.
• Highly intuitive UX: Users can search, sort, and respond to alerts, all from Nightfall’s highly intuitive console. With Nightfall’s dynamic pop-out previews, security teams can see meaningful context and metadata surrounding each violation with just a single look. For an added bonus, teams can also opt to remediate violations from within this preview panel. This feature saves valuable energy by eliminating the need for context-switching. But to deliver a truly seamless workflow, Nightfall can also leverage AI and webhooks to send automated alerts to security teams via email, Jira, Slack, or their SIEM of choice. Each alert provides actionable context and metadata so that security teams can remediate violations without having to leave their communication app.

Response

When a violation occurs, every second counts. Here are three ways that AI features can significantly reduce security teams’ response time:

• Priority escalation: Nightfall’s newest feature—global violation search—makes it easy for teams to pinpoint violations due to specific criteria, including high-priority rules and policies. However, if teams are looking to streamline their findings even more, they can tune their detectors to have a higher threshold for “Minimum confidence” or “Minimum number of findings.”  By training detectors to ignore low-risk alerts, Nightfall helps security teams to prioritize pressing violations while also reducing alert fatigue.
• Automated remediation: In Nightfall’s latest product release, Nightfall for ChatGPT, end-users are able to remediate their own violations. This has a threefold effect. First, it alerts end-users in real time to stop violations before they happen. Second, it provides an opportunity for end-users to learn about company policies in highly specific situations. And last, but not least, it puts a significant dent in security team workloads.
• End-user education: There’s only one surefire way to reduce instances of accidental data sharing over time, and that’s by creating a strong culture of security. By automating employee education in context-specific situations (as opposed to generic security training sessions), end-users will be more likely to remember and follow company policy moving forward. As a result, this will chip away at security teams’ to-do lists.

Conclusion

As risks continue to evolve, security teams must leverage cutting-edge tools to stay ahead of the curve. Fortunately, AI-powered security solutions like Nightfall are here to help. By streamlining workflows and automating time-consuming tasks like installation, monitoring, and response, tools like Nightfall empower security teams to focus on what they do best: Protecting their organizations from threats.