These days, the security operations center isn’t a fun place to be. According to research, 84% of security professionals are burned out and overwhelmed by alert fatigue, false alarms, and limited visibility into the technology stack.
As a consequence, the cybersecurity skills gap increased by 12% in the last 12 months, with a mass exodus of skilled professionals leaving the industry and not enough rising up the ranks to take their place.
Of course, this only exacerbates the issue. Less team members means analysts have to work harder and longer, which amps up the risks of burnout, which increases the likelihood of quitting.
Something’s got to give.
Thankfully, generative AI might just be the SOC’s knight in shining armor.
The modern SOC has outdated workflows
Working in the SOC wasn’t always a barrage of alerts. Before the revolution of cloud computing and SaaS apps like Slack and Microsoft Teams, enterprises fortified their organizations’ security with solutions like firewalls, spam filters, and VPNs.
The mindset back then was one of a ‘castle-and-moat’ approach to security. The idea was to keep attackers outside of the company walls at all costs. During this period, the enterprise network was well-contained, manageable, and visible, making security analysts’ jobs linear and clear in tandem.
But the world of work has changed a lot since then. We’re now in the work-from-anywhere era, where employees engage with a wealth of SaaS applications across devices and from different locations. There is no castle. And no moat. The perimeter is now borderless.
To secure this complex new landscape, zero trust has risen as a critical approach to protecting sensitive data and mitigating identity compromise. However, a zero-trust architecture demands a lot from security analysts, who need to analyze an almost constant stream of data points and alerts (many of which are false!) to maintain optimum security.
Generative AI can alleviate the burden
In the same way that generative AI is making functions like marketing, sales, and customer service more efficient, it can help the SOC to combat alert fatigue, boost accuracy, and regain control of their security stack.
Here are the top use cases we recommend:
Data loss prevention
Natural language processing (NLP) can enhance the efficiency and accuracy of data loss prevention (DLP) solutions, enabling organizations to automate the process of discovering, classifying, and protecting unstructured data in collaborative SaaS apps while minimizing false positives.
For example, Polymer DLP harnesses the power of generative AI to seamlessly and intelligently redact unstructured, in-motion sensitive data within apps like ChatGPT, Slack, and Microsoft Teams.
Threat hunting
With organizations relying on numerous security tools for threat hunting, the sheer amount of data and alerts they have to parse through is impossible to manage. Generative AI can be used to consolidate this data into one repository, making it much easier to prioritize security incidents.
Reporting
Generative AI can simplify and automate the report drafting process, alleviating the time-consuming task of writing up reports for SOC 2 audits, stakeholder meetings, and compliance reviews.
Security training
Generative AI is revolutionizing security awareness programs by offering point-of-violation training to users who might inadvertently or intentionally violate data protection policies. For example, Polymer DLP provides real-time nudges to users when they engage in risky behavior. This approach has shown remarkable results, reducing repeat violations by over 40% within days.
Getting started
Although investing in generative AI for security can be costly and time-consuming, it doesn’t have to be.
There are tools out there, like Polymer DLP, that are low-code and plug-and-play—meaning your security team can start benefiting from the power of generative AI in just minutes.
Polymer uses advanced NLP algorithms and models to discover, classify, and secure sensitive data across your SaaS applications and generative AI tools. Here’s how it alleviates stress in the SOC:
- False positive reduction: Polymer DLP is designed to overcome the traditional pitfalls of DLP, offering high true positive ratios thanks to the fusion of natural language processing and regular expressions.
- Automatic remediation: Using a self-learning engine, our tool autonomously remediates potential instances of data exposure without the need for manual intervention, meaning your security team can focus on strategic work instead of getting caught up in responding to alerts.
- Zero trust enablement: Polymer DLP uses dynamic, contextual authentication factors to verify users as they request access to sensitive information in real-time, bringing the principles of zero trust to your applications, cloud storage platforms like Box, ticketing systems & codebases.
- Quantifiable value: Demonstrating the value of security investments has long been a challenge, but our data exposure risk score changes the game. It’s a metric that quantifies the presence of sensitive data, both inside and outside the organization. This score lends a measurable edge to data loss prevention efforts and allows for an accurate ROI calculation.
- Culture of security: Polymer DLP supports point-of-violation training with real-time nudges to user who violate security policies. This approach has proven to reduce repeat violations by over 40% within days.
Ready to improve your security posture and give your security team some time back? Request a demo of Polymer DLP today.
