AI In Cybersecurity: Looking Beyond The SOC
Artificial intelligence (AI) has become more than a buzzword in cybersecurity. Every new vendor, startup, and product marketing pitch touts advanced algorithms, machine learning, and real-time threat detection. While there’s no denying that AI can streamline operations in the Security Operations Center (SOC), an overemphasis on SOC-centric use cases creates unintended blind spots. The bulk of AI-driven innovation today aims to automate incident detection, speed up alert triage, and drive machine-scale resolutions for immediate threats. Although these capabilities are critical, focusing too heavily on the detect-and-respond phase overlooks the foundational predict-and-prevent side of cybersecurity. We’ll explore why the SOC is such a magnet for AI, what we risk by confining new technologies to that arena, and how the industry can shift toward a more proactive and holistic security strategy.
Why the SOC Attracts Most AI Investments
The Security Operations Center is often described as the beating heart of an organization’s defense. SOC analysts are on the front lines of the threat landscape, grappling with high volumes of alerts that pour in around the clock. The promise of AI in this environment is clear: reduce noise, correlate disparate data, and help analysts zero in on genuine threats in real time.
From a business perspective, it’s relatively straightforward to measure the ROI of AI in the SOC. Metrics such as “mean time to detect” (MTTD) and “mean time to respond” (MTTR) visibly improve when AI tools optimize alert handling. This streamlined, reactive approach is gratifying because it delivers immediate, tangible wins: a previously missed intrusion can be detected and contained in minutes instead of hours or days. Maximizing those metrics can be misleading.
The Consequence of a Narrow Focus: Reactive Overload
Yet this emphasis on detection and response, while beneficial, can inadvertently perpetuate a reactive culture. When AI is limited to the SOC, organizations end up constantly putting out fires rather than addressing the conditions that allow these fires to start in the first place.
For instance, many threats succeed because of common, preventable issues: unpatched software, misconfigured cloud settings, excessive user privileges, or endpoints missing the latest security updates. If insights from the SOC never loop back into hardening the organization’s infrastructure, these vulnerabilities remain unchecked. Similarly there’s the concept of where we can learn the root causes and feed those back into the preventative side. The result? Analysts might be seeing faster detections, but the volume of incoming incidents never actually decreases, leading to the same “alert fatigue” AI was supposed to alleviate.
Over time, this reactive treadmill becomes a drain on budgets and human resources. Continually investing in tools that address “the last breach” shortchanges preventative disciplines—like vulnerability management, identity governance, and robust configuration practices—that are vital to stopping a breach before it even starts.
Machine-Scale Resolutions vs. Human-Centric Prevention
One of the biggest selling points of AI in the SOC is machine-scale resolution—the ability to make automated decisions in milliseconds, isolating compromised hosts or blocking malicious IP addresses without waiting for a human to intervene. While these capabilities can be a game-changer in emergencies, relying too heavily on automated containment processes can cause complacency.
Organizations might see fewer headline-grabbing incidents thanks to these rapid responses, but still remain vulnerable if they neglect the baseline “cyber hygiene” needed for long-term resilience. Automated responses are not a substitute for systematic improvements in patch management, user training, configuration standards, and other preventive measures. Think of it like a high-tech sprinkler system: it’s great at quickly dousing small fires, but not if you keep storing flammable materials in the same place day after day.
The Predict-and-Prevent Imperative
To break free from the cycle of perpetual firefighting, it’s critical to harness AI for more proactive endeavors. The same machine learning algorithms that excel at correlating alerts in real time can also analyze trends to forecast emerging risks. For example, some AI-driven platforms can predict which newly discovered vulnerabilities are most likely to be weaponized, allowing security teams to prioritize patching accordingly.
Similarly, anomaly detection engines that spot unusual network behaviors could also flag chronic misconfigurations in cloud services or endpoints. By feeding these insights into a broader security ecosystem—ranging from identity and access management to risk assessment tools—organizations can begin to fortify weak spots before an attacker exploits them.
Predict-and-prevent strategies also reduce the overhead on the SOC. If a vulnerability is patched preemptively, that’s one less potential incident to detect, analyze, and resolve. This synergy underscores why AI must bridge the gap between “operations center” and “organization-wide resilience.”
Key Steps to Integrate AI Beyond the SOC
1. Close the Loop on Data: When AI tools in the SOC detect repeated types of attacks or highlight specific vulnerabilities, that intelligence should automatically feed into patch management workflows, configuration baselines, and risk registers.
2. Adopt Comprehensive Platforms: Consider solutions that unify detection with prevention. Next-generation SIEMs and SOAR platforms often integrate with asset management and vulnerability scanning tools—just ensure your AI and ML tools are configured to share data across these modules.
3. Foster a Collaborative Culture: Technical solutions alone are never enough. Security, IT operations, DevOps, and compliance teams must collaborate to ensure that prevention tasks (e.g., patching, system hardening) get the same priority as incident response.
4. Invest in Cyber Hygiene Automation: Automated scanning for outdated software, suspicious cloud settings, or insecure protocols can proactively reduce your attack surface. This blend of hygiene automation and AI-based insights stops many alerts from arising in the first place.
5. Align Budget and Metrics: If all funding goes toward incident response tools, prevention will suffer. Senior leadership should allocate resources to both detection and hygiene, measuring success not only in how quickly threats are contained but also in how many potential threats are eliminated preemptively.
The Path Forward
AI-driven SOC solutions have brought unprecedented efficiency and speed to incident response. But if the cybersecurity community remains fixated on the reactive side, we risk ignoring a massive opportunity to get ahead of attackers. Prevention and prediction require the same level of innovation that detection has received in recent years. By directing AI’s analytical prowess toward systemic vulnerabilities, configuration lapses, and strategic risk assessments, companies can reduce the overall flood of alerts that burden the SOC, ultimately freeing analysts to focus on the truly critical threats.
Shifting from a “response first” to a “prevention first” mindset demands both technological investments and cultural changes. It calls for seamless integration of findings into every layer of security—from patching schedules to user education programs. In doing so, organizations can break the cycle of constant firefighting and usher in an era where the SOC is not only the last line of defense, but also a powerful engine for continuous improvement across the entire security landscape.
The original article first appeared in ForbesLink to https://www.forbes.com/councils/forbestechcouncil/2025/03/03/ai-in-cybersecurity-looking-beyond-the-so/.
