COMMENTARY: The byproduct of AI hype is AI slop. Consider shoddily made videos and reels littering the internet, which look good at first glance before you realize they were tossed together with cheap AI tools. While it’s annoying for the average person, when this behavior invades important industries, such as cybersecurity, it becomes a greater concern.Unfortunately, we are seeing this play out in real time. The market has been awash with poorly constructed AI solutions that are, in large part, designed to merely find and collect more vulnerabilities. While these sound cool on the surface, they won’t improve security if we don’t improve our prioritization and remediation processes. [SC Media Perspectives columns are written by a trusted community of SC Media cybersecurity subject matter experts. Read more Perspectives here.]Security teams aren’t dealing with a lack of alerts; they’re suffering from an overload. In a security context, AI-generated slop is creating a crescendo of alert noise that increases workload rather than reducing it. We don't just want summaries without insights, generic remediation guidance without understanding the environmental risks, or duplicate alerts across fragmented tools. Already buried in alerts, security professionals don't need dodgy recommendations based on shoddy, incomplete data. In short, if your AI only produces more tickets, alerts, and noise, it’s AI slop: the empty calories of automation, momentarily satisfying, but ultimately hollow.
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In practice, contextual noise reduction is where agentic systems can make a huge impact, especially by correlating findings across different systems and environments to mitigate duplicate alerts. It can also help prioritize where your focus should be, identify real exposure versus just theoretical risk, and result in fewer but highly confident tickets.Slop can present itself as confident, poised language, but it’s often not verified — this is where it can move from nuisance to danger. An AI system designed to know when to avoid overextending itself will mitigate unverified vulnerability claims, reducing needless escalation. It achieves this by correlating actual exploitability, real business impact, attack and blast radius, and security activity on threats to allow teams to focus on the vulnerabilities that matter, not the hundreds that may exist. Once the focus is clear, you can turn to safe, verified remediation. This step goes far beyond generating a remediation script for yourself. Agentic AI can help ingest contextual information about the affected systems and components, construct a plan, consider side effects and failures, include remediations within battle-tested automation frameworks (which nicely ties this to your operations and engineering teams), and finally roll out and verify results. You don’t need to blindly trust the AI — it’s built with guardrails and rollback mechanisms to avoid automation drift (the slow, unintentional shift of decision-making power from humans to AI).These steps are crucial and demonstrate how agentic systems differ from mere GenAI slop. Owning the workflow and the results means you are far less likely to act on unverified output that introduces remediation risks.
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