Generative, Agentic, or Predictive AI? A Guide to Choosing the Right Solution
As cybersecurity threats grow in complexity and volume, security operations centers (SOCs) and security operators in general are on a constant quest to find advanced tools and methods to enhance the capabilities of their security information and event management (SIEM) systems. Many, if not all, of these solutions claim to improve security by addressing specific concerns in an efficient way: detecting anomalous behavior more quickly and accurately, reducing the number of alerts, remediating vulnerabilities, detailed reporting, etc. All with the stated intent of making security professionals better at what they do and, by extension, improving the organization’s security.
Among the most discussed technologies are solutions utilizing generative AI, particularly when leveraged within “agentic” systems (AI systems designed to act autonomously, make decisions, and pursue outcomes, rather than simply responding to defined inputs), and predictive AI solutions (AI that maps vast datasets to recognize and take action on behavior patterns, trends, and associations). While both offer security teams significant promise in their own respective ways, understanding their fundamental differences and optimal applications is foundational for SOC professionals and security operators.
How AI (and agents) are best used in cybersecurity
Agents prove particularly valuable when the SOC needs to dynamically determine the workflow of a cybersecurity solution. This flexibility requires a more adaptive response to unforeseen or complex scenarios. However, it’s crucial to assess if such flexibility is truly necessary or if it introduces additional complexity.
The fundamental question is this: Do you need a highly flexible workflow to efficiently solve the cybersecurity task at hand? If a pre-determined workflow consistently falls short, then the answer to this question is “yes” and greater flexibility is indeed required.
Consider a cybersecurity application designed to triage incoming security alerts. If you can anticipate that those alerts will consistently fall into a few predetermined categories, you can create a specific, hard-coded workflow for each. For instance:
- Known Malware Alert? Automatically quarantine the affected endpoint and generate a standard incident ticket.
- Routine Software Update Notification? Archive the alert after logging for compliance.
If these deterministic workflows effectively address the vast majority of incoming alerts, then hard-coding these responses is the most reliable approach. This could effectively eliminate the risk of errors introduced by letting generative AI’s unpredictability complicate your workflow. For simplicity and robustness, it’s often advisable to avoid agentic behavior where a fixed workflow will suffice. If not then you might be looking for something different.
How can AI agents benefit the cybersecurity ecosystem?
Rule-based workflows are great in standard situations. However, if the workflow cannot be determined well in advance due to the complexity and variability of the cybersecurity incidents being handled by the system, an agentic setup becomes highly beneficial.
For example, consider a complex security query such as, “I’m observing unusual outbound traffic to an unknown IP address from our critical financial server, correlating with multiple failed login attempts on that server, and I see a new, unrecognized process running. What’s the immediate impact, and what should I do?”
This query involves multiple, interconnected factors that don’t fit a simple, predefined category. In such a case, a multi-step agent could:
- Access SIEM/Log Management APIs: To pull detailed logs of network connections, authentication attempts, and running processes from the financial server.
- Integrate with threat intelligence feeds: To check the reputation of the unknown IP address and any identified hashes of the new process.
- Consult an incident response playbook knowledge base (Retrieval-Augmented Generation or RAG system): To identify relevant procedures for such a combination of indicators.
- Query an asset management database: To understand the criticality and dependencies of the financial server.
- Generate a comprehensive situational analysis and recommend immediate, tailored actions: Such as isolating the server, blocking the suspicious IP, initiating forensic imaging, and notifying the incident response team.
This ability to dynamically assess, gather context from disparate systems, and respond to unpredictable, real-world cybersecurity scenarios is where agentic systems truly shine.
Stop chasing, be one step ahead
The amount of vendors claiming their AI can augment existing processes has multiplied exponentially over the last year. However, our industry is still bringing a multitude of enhancements that appear to be too reactive in nature: focusing on identifying anomalies and prioritizing alerts after a security event has emerged. The intention is to help in processing and understanding existing data, making security analysts more efficient in their reactive duties.
But are these solutions truly agentic? Remember, we defined agentic as AI systems designed to act autonomously, make decisions, and pursue outcomes, rather than simply responding to defined inputs.
The most promising direction for AI in security lies in designing systems from the ground up with AI as a core component, building intelligence directly into the architecture for adaptive learning and continuous improvement. Take the example of solutions that parse and analyze hundreds of terabytes of data to correlate data-points to uncover coordinated attacks, even faster than the attackers themselves can launch them. In other words, using AI for what it is good at and letting human teams focus on the activities that technology simply cannot replace.
The BforeAI difference: The promise of predictive security
At BforeAI, we understand that while generative AI is great for specific applications, cybersecurity demands more. We are focused on predictive security. Our approach is fundamentally different from relying on workflow enhancement and, instead of merely identifying anomalies or reacting to known threats, our PreCrime™ technology predicts attacks by analyzing network behavior, identifying emerging malicious web infrastructure, and anticipating future attack patterns by blocking them automatically as a true agentic solution should do. This is achieved by building intelligence directly into the architecture, enabling adaptive learning and continuous improvement – a critically important distinction versus the practice of simply adding AI to existing processes.
For security operators and SOC teams, this distinction is critical. While AI-powered agents can dramatically improve incident response and threat analysis by providing dynamic, context-aware assistance, they primarily operate on observed data. Predictive AI, on the other hand, aims to prevent attacks before they materialize, offering a preemptive layer of defense that complements and enhances the traditional reactive capabilities of a SOC. The results are truly “left of boom”.
In essence, using AI in any of its forms will lead to improvements in the efficiency and effectiveness of your security workflows. Predictive AI, however, represents a paradigm shift, enabling your organization to move beyond reactive security to a truly preemptive posture, minimizing the window of opportunity for attackers and significantly reducing overall risk.
