The advent of generative AI and its seemingly endless possibilities has also introduced a myriad of sophisticated threats. As organisations transition to increasingly complex digital environments and struggle to analyse the exponential growth of security data, Security Information and Event Management (SIEM)1 solutions are evolving to harness the power of generative AI. Many businesses are now asking: Should we implement generative AI?
To answer this question, we must consider multiple factors.
The Evolution of SIEM
To envision what the future could hold with generative AI, let’s first take a brief look at the past. In response to growing network traffic, SIEM emerged in the early 2000s, marking the first time security practitioners combined information and event management into one comprehensive strategy. To adapt to a constantly changing landscape, SIEM evolved into a tool capable of identifying genuine threats in real-time. Its ability to gather and analyse thousands of security alerts from various tools—such as firewalls, antivirus software, and intrusion detection systems (IDS)—was revolutionary.
Machine learning2 (ML) has been integrated into security tools for years, first appearing in anti-malware software and then expanding to broader anomaly detection for networks and users. While anomaly detection remains a cornerstone of SIEM’s evolution, in modern environments, SIEM tools often generate an overwhelming number of alerts. This has turned them into “alert factories” rather than actionable tools. Security professionals—whose numbers are insufficient to meet the growing demand—are left to manage these alerts. Modern security analytics and generative AI now aim to bridge this gap.
The Skill Shortage: A Cybersecurity Vulnerability
The cybersecurity workforce shortage has reached a record high of just under 4 million3, even as the global cybersecurity workforce grew by almost 10%4 last year. A World Economic Forum report revealed that while India produces one-third of the world’s graduates in science, technology, engineering, and mathematics (STEM), 30%5 of its 40,000 cybersecurity job vacancies remained unfilled in 2023 due to a lack of skilled talent.
The reasons behind this shortage are multifaceted. Cybersecurity professionals face increasingly complex workloads6, smaller teams, and tighter budgets, all while contending with a growing threat landscape and stringent regulatory requirements. Smaller budgets also hinder organisations’ ability to onboard new talent and develop their internal pipelines. Furthermore, a widespread misconception exists that cybersecurity roles require a technical background, discouraging individuals from diverse or nontraditional paths who might otherwise excel as security analysts.
Generative AI can help bridge this labour gap while addressing evolving threats. By combining its advanced data-processing capabilities with proprietary data and a powerful search engine through retrieval-augmented generation (RAG)7, organisations can empower professionals without requiring them to have extensive domain knowledge.
This technology allows a broader range of professionals to contribute to cybersecurity, leveraging generative AI’s capabilities to perform business-critical tasks. The result? A more diverse and effective workforce.
How Generative AI Can Work for Your Cybersecurity Team
You cannot protect what you cannot see. In today’s distributed environments, where data volumes are continuously expanding, the lack of cross-stream visibility remains a significant challenge for security professionals. A unified data platform is critical to overcoming this issue. Generative AI, when paired with search technology, revolutionises how IT, cybersecurity, and business users interact with data across channels.
Generative AI provides conversational search capabilities, improving visibility, analytics, and response speed. Whether automated for background analytics or used as a searchable knowledge repository, generative AI enriched with proprietary data becomes a versatile tool for various security applications.
Here’s how generative AI can enhance cybersecurity:
- Force Multiplier: Generative AI amplifies the capabilities of existing cybersecurity professionals while making complex tasks more accessible to junior analysts through natural language interfaces.
- Data Synthesis: It synthesises and analyses vast amounts of threat data, compensating for the limited availability of human threat analysts.
- Enhanced Detection: AI models significantly improve anomaly detection, identifying unusual behaviours across processes, users, and devices.
- Predictive Analysis: Generative AI proactively identifies potential vulnerabilities, offering solutions before human experts even recognise the threat.
- Automated Reporting: It generates automated feedback and insights, ensuring that today’s findings inform tomorrow’s strategies.
The power of natural language search for improving security resilience cannot be underestimated. Back to a common security dilemma: an alert goes off and an event is detected—what’s next? In this scenario, a security professional aided by generative AI can query the system for actionable insights—pulling information from public and private datasets to reduce response and resolution times.
The Challenges of Using Generative AI for Cybersecurity
Despite its potential, generative AI has its limitations. One significant concern is the risk of hallucinations—instances where AI generates incorrect or irrelevant outputs. Contextual enrichment through RAG can mitigate these errors, but even this approach is not foolproof.
Additionally, generative AI cannot function without human oversight. While it can alleviate the skills gap and personnel shortage, it cannot replace human expertise. Effective threat detection, investigation, and response (TDIR) frameworks must already be in place for generative AI to enhance them. Rather than acting as a replacement for security operations centres, generative AI serves as an assistant and accelerator.
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The Future of Generative AI in Cybersecurity
The Elastic Global Threat Report highlights how enterprises migrating to cloud environments are vulnerable to misconfigurations, lax access controls, unsecured credentials, and inadequate implementation of the principle of least privilege (PoLP). With the speed and stealth of threat actors8 increasing, generative AI offers a critical advantage. Properly implemented, it can counter malicious activities and level the playing field for defenders.
Generative AI is reshaping the cybersecurity workforce, redefining skill sets, and expanding opportunities for individuals with diverse backgrounds. By making technical capabilities more accessible, the technology transforms how organisations approach cybersecurity.
So, should your organisation implement generative AI? The answer is likely yes. Providing your analysts with tools to address the skills shortage and counter a borderless threat landscape is essential to ensuring your organisation’s resilience in an ever-evolving digital world.
Views expressed by: Mandy Andress, Chief Information Security Officer, Elastic