The pitch for autonomous security agents sounds great until you think about it for thirty seconds.
"AI that investigates and responds to threats automatically, 24/7."
Now imagine explaining to your board that an AI quarantined 200 endpoints during a false positive at 2 AM. Or that an automated response blocked your CEO's account during a legitimate overseas login. Or that you can't actually explain what the system did because it "reasoned" its way to a decision without logging the intermediate steps.
CISOs are right to be skeptical. Autonomy without oversight can quickly turn into negligence with extra steps.
But the opposite extreme doesn't work either. If every agent action requires human approval, you've just built a faster way to generate tickets. The bottleneck moves from "analyst doing the work" to "analyst approving the work." You've added latency without adding capacity.
The question isn't whether to include humans. It's where to include them.
Vendors tend to present two options:
Full autonomy: The AI handles everything. Humans get notified after the fact. Speed is maximized, but control is gone. One bad decision cascades before anyone notices.
Full oversight: Every action requires approval. Control is maximized, but speed is gone. You've essentially built a recommendation engine that creates more work for already-overloaded analysts.
Neither model fits how security operations actually work.
Some decisions genuinely require human judgment—not because AI isn't capable, but because accountability demands it. Quarantining a production server. Disabling an executive's account. Initiating legal hold on an employee's data. These actions carry organizational consequences beyond the security team's scope.
Other decisions don't. Enriching an alert with threat intel. Querying additional log sources. Correlating related events. Generating a timeline. These are investigative steps, not consequential actions. Requiring human approval for each one defeats the purpose of automation.
The goal is surgical placement of human oversight: maximum autonomy for investigation, explicit approval gates for response.
Strike48 agents operate with a clear boundary: investigate autonomously, respond with approval.
Autonomous by default:
An L1 Analyst Agent can receive 200 alerts, correlate them into three distinct cases, investigate each one, determine two are false positives and one is an active phishing campaign, and produce a complete investigation report—without a human touching anything.
Approval required:
The agent does the work. The human authorizes the impact.
This isn't a philosophical position—it's a workflow architecture. Each agent action is categorized as investigative or consequential. Investigative actions execute immediately. Consequential actions queue for approval with full context attached.
Bad approval workflows present a binary choice with minimal context: "Quarantine endpoint Y/N?"
That's not human-in-the-loop. That's human-as-rubber-stamp. The analyst doesn't have enough information to make a real decision, so they either approve everything (defeating the purpose) or spend twenty minutes gathering context (defeating the speed benefit).
Strike48 approval gates include everything the agent knows:
The human makes an informed decision in seconds, not minutes. They're reviewing the agent's judgment, not reconstructing its work.
Here's where most "agentic" products fall apart under enterprise scrutiny: explainability.
When an agent "reasons" its way to a conclusion, what actually happened? Which data did it consider? What queries did it run? Why did it prioritize one hypothesis over another? If you can't answer these questions, you can't defend your security program in an audit, a board meeting, or a courtroom.
"The AI decided" isn't an acceptable answer.
Strike48 logs every agent action with SIEM-grade lineage:
This isn't a summary generated after the fact. It's a complete, immutable record built as the agent operates. You can reconstruct any investigation step-by-step, see exactly what the agent "saw" at each decision point, and understand why it reached its conclusions.
When your auditor asks how you detected a breach, you don't say "our AI found it." You show them the detection rule that fired, the alerts that correlated, the queries that ran, the evidence that was collected, and the reasoning chain that led to escalation. Every step documented, every decision traceable.
Regulatory frameworks increasingly require explainability for automated decision-making. GDPR's right to explanation. SEC's cybersecurity disclosure rules. Industry-specific requirements around incident response documentation.
If your security automation can't produce audit trails that satisfy these requirements, you're trading one compliance problem for another.
Strike48's architecture treats audit logging as foundational, not optional:
The agents work autonomously. The audit trail proves it.
The operational impact comes down to where time gets spent.
Without agents: Analysts spend 80% of time on investigation, 20% on decision-making. Most alerts never get investigated. Time to respond is measured in hours or days.
With agents + bad human-in-the-loop: Analysts spend 60% of time approving routine actions, 40% on actual decisions. The queue shrinks slightly, but analyst capacity is still the constraint. Time to respond improves marginally.
With agents + surgical approval gates: Agents handle 100% of investigation autonomously. Analysts spend 100% of their time on decisions that actually require human judgment. Time to investigate drops to minutes. Time to respond depends only on how fast humans approve consequential actions.
The goal isn't removing humans from security operations. It's removing humans from tasks that don't require human judgment—so they can focus entirely on tasks that do.
See the audit trail for yourself. Request a demo to watch Strike48 agents investigate real alerts and see exactly how approval gates and logging work in practice—autonomous speed, complete accountability.