Burnout among security leaders and professionals is reaching unprecedented levels. As organizations’ digital landscapes become increasingly complex—spanning the cloud, generative AI, and evolving threat tactics—security teams are struggling to keep pace. Many are feeling overwhelmed by the sheer volume and sophistication of cyber threats, and some are even leaving the profession altogether.
The good news is that there’s a way forward. Just as cybercriminals are harnessing AI to bolster their attack strategies, security teams can tap into this same technology to significantly enhance efficiency and effectiveness.
However, treating AI as a simple “bolt-on” tool is a missed opportunity. This technology, which is one of the most transformative innovations since the advent of the internet, holds the potential to fundamentally reshape how Security Operation Centers (SOCs) operate.
For AI to truly unlock its full potential in the SOC, it needs to be integrated as a core component of operations—not just an afterthought. This is where the philosophy of human-AI teaming comes into play.
What is human-AI teaming?
Human-AI teaming involves reimagining AI as a team member, working alongside employees in a coordinated, interdependent way. Instead of viewing AI as just another tool, organizations adopting this approach treat the technology as an active collaborator that complements human capabilities.
In the coming years, human-AI teaming will become an integral part of everyday work. Employees are already leveraging tools like ChatGPT and Bard to boost productivity, and as specialized AI applications emerge, the potential for collaboration will only expand.
SOCs, in particular, stand to gain significantly by adopting a human-AI teaming model. AI can handle high-volume, repetitive processes, freeing up human analysts to focus on critical decision-making and more complex threats—ultimately creating a SOC that’s faster, more efficient, and better equipped to stay ahead of cybersecurity challenges.
The state of the SOC
Even with AI-driven tools becoming increasingly common in SOCs, many SOCs still face core challenges that impact their ability to manage and respond to cyber threats effectively.
One of the biggest hurdles is the need for skilled staff. Security roles require specialized expertise, and with talent shortages across the industry, it’s tough to find the right people to fill the roles needed to support 24/7 SOC operations.
In fact, 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. This shortage often leads to burnout for those already on board, who are left to shoulder the burden of constant threat monitoring. It’s no wonder that 84% of security professionals say they are burned out and overwhelmed.
To make matters worse, manual processes are still a reality for many SOCs, even as threats multiply. Without adequate automation, security teams struggle to keep up, often dealing with siloed tools and limited enterprise-wide visibility. The lack of cohesive processes and playbooks can mean longer response times and missed opportunities to contain incidents quickly.
At the same time, SOC teams are flooded with alerts day in, day out—and without intelligent correlation, each alert demands attention. Add in numerous unintegrated tools and insufficient context around incidents, and you have a recipe for inefficient decision-making.
When analysts are overwhelmed by constant alerts without clear prioritization, it can lead to alert fatigue—a state of mental exhaustion that reduces responsiveness and effectiveness. But alert fatigue doesn’t just affect individual analysts; it ultimately threatens the organization’s overall cybersecurity posture.
AI’s potential in the SOC: Key areas to focus on
According to RSA, only one in three organizations is using AI to boost cybersecurity efforts. Yet, AI has the potential to significantly improve SOC efficiency. With AI-powered solutions, SOCs can gain the context they need to respond to incidents accurately, reduce the chances of alert fatigue, and enhance overall security.
However, the road to human-AI teaming isn’t without challenges. AI-led tools often come with a hefty price tag, and Gartner predicts that nearly 30% of AI pilots will fail in the coming year. To avoid these pitfalls, cybersecurity leaders should approach AI adoption with a strategic mindset, avoiding overly complex or ambitious projects that heighten the risk of failure. Starting small with high-impact, low-risk AI technologies is a smart way to enhance SOC efficiency and build momentum.
Here are the top AI-driven use cases to consider for an immediate boost to SOC operations.
Data loss prevention
With natural language processing (NLP), AI-driven data loss prevention (DLP) solutions are becoming smarter and more accurate, automating the process of discovering, classifying, and safeguarding unstructured data across collaborative SaaS platforms.
Tools like Polymer DLP leverage generative AI to seamlessly and intelligently redact sensitive data in real time within applications like ChatGPT, Slack, and Microsoft Teams, where unstructured data is commonly shared. By minimizing false positives, AI ensures that SOC teams can focus on real threats, rather than getting bogged down with a flood of alerts.
Threat hunting
For SOCs managing numerous security tools, the flood of alerts and data can be nearly impossible to handle manually. Generative AI offers a solution by consolidating information from multiple sources into a centralized repository, giving SOC analysts a clear and prioritized view of incidents. This centralization allows AI to filter out lower-priority issues, presenting analysts with the most pressing threats first and enabling them to focus on targeted, strategic threat hunting.
Reporting
Generative AI is making report generation faster and more accurate, streamlining tasks that typically consume a significant amount of time. By automating the creation of reports for audits, stakeholder meetings, and compliance reviews, AI reduces the burden of documentation on SOC teams. Analysts can redirect their time toward threat assessment and response, while AI ensures that reports remain consistent and meet compliance standards.
Human risk management
AI is playing a pivotal role in helping security teams build a strong culture of security through human risk management (HRM). HRM is a strategic approach that focuses on identifying, evaluating, and educating employees about security threats in real-time, with the ultimate goal of transforming employees from potential risks into active protectors of the business.
HRM is inherently AI-driven, as it relies heavily on automation to provide immediate, relevant guidance to employees as they encounter or trigger potential threats. For example, Polymer DLP harnesses the power of AI to provide active learning nudges. These nudges are context-aware, delivering real-time reminders or guidance when employees engage in potentially risky behavior, such as sharing sensitive data via unsecured channels or mishandling access controls.
Over time, these consistent, real-time interventions help instill a security-conscious mindset throughout the organization, turning every employee into a proactive participant in the company’s cybersecurity efforts.Unlock the potential of human-AI teaming in your SOC with Polymer DLP. Request a free demo now.
