The Broken Promise of SOAR
For the last decade, the cybersecurity industry has relied on Security Orchestration, Automation, and Response (SOAR) platforms to handle the deluge of alerts. The premise was simple: if we can map out every possible attack vector, we can write a linear playbook to handle it.
However, the reality of 2025 is starkly different. Attackers do not follow static playbooks. They pivot, they persist, and they modify their behavior in real-time. A static playbook configured to block an IP address is useless against a polymorphic attack that changes infrastructure every 30 seconds.
""Static playbooks are the Maginot Line of cybersecurity—impressive on paper, but easily bypassed by a dynamic adversary who simply goes around them.""
The result is a burned-out SOC team. Tier 1 analysts spend 80% of their day manually validating false positives that their "automated" tools failed to contextually understand. We need a fundamental shift from automation to autonomy.
Enter Agentic AI
Agentic AI differs from standard Large Language Models (LLMs) or scripts because it possesses agency. While a script executes a command, an Agent perceives its environment, reasons about the state of the system, and decides on the best course of action to achieve a goal.
In the context of ThreatLens, we are no longer building linear scripts. We are building a Virtual SOC Team comprised of specialized agents.
- The Hunter Agent: Proactively scans logs for behavioral anomalies that match TTPs (Tactics, Techniques, and Procedures) rather than just signatures.
- The Analyst Agent: Enriches alerts by querying external threat intel sources, vector databases, and internal asset managers to build context.
- The Responder Agent: Formulates a remediation plan, tests it in a sandbox environment, and executes it upon approval.
Case Study: The Phishing Pivot
Let's look at a real-world scenario. A user reports a suspicious email.
In a traditional SOAR setup, a playbook extracts the URL, checks it against VirusTotal, and if it is clean, closes the ticket. This is a failure point. Sophisticated attackers use clean URLs that redirect to malicious payloads only after the email has landed.
Here is how an Agentic System handles the same event:
- The Analyst Agent observes the URL is clean but notices the domain was registered only 24 hours ago (an anomaly).
- It decides to investigate the sender. It queries Microsoft Graph API and realizes the sender usually emails from New York, but this email originated from a VPN in Lagos.
- It instructs the Hunter Agent to check if any other users received emails from this sender.
- It finds 50 other emails. It correlates this with a sudden spike in CPU usage on three endpoints.
- The Response Agent concludes this is a coordinated campaign, isolates the three affected machines, and purges the email from 47 other inboxes before they are opened.
This entire process happens in seconds, without human intervention, using reasoning rather than hard-coded logic.
The Role of Memory and Context
One of the biggest limitations of traditional tools is amnesia. They treat every alert as an isolated incident. Agentic AI utilizes Long-Term Memory (Vector Databases) to recall historical context.
If an agent sees an attempted SQL injection today, it remembers that a similar IP range probed the firewall three months ago. It connects the dots that a human analyst might miss due to shift changes or alert fatigue.
""Intelligence is not just about processing speed; it is about the ability to connect two seemingly unrelated data points across time.""
Navigating the Trust Gap
The biggest barrier to adopting Agentic AI is not technology; it is trust. CISOs are rightfully wary of letting an AI make changes to firewall rules or lock out user accounts autonomously.
To solve this, we advocate for a "Human-in-the-Loop" (HITL) architecture during the initial deployment phase.
- Level 1 Autonomy: The AI investigates and presents a summary and a recommended action button to the analyst.
- Level 2 Autonomy: The AI executes low-risk actions (like password resets) but asks for permission for high-risk actions (like server isolation).
- Level 3 Autonomy: The AI acts fully autonomously but logs every decision to an immutable ledger for audit and rollback purposes.
Conclusion
The era of the linear playbook is ending. The volume and velocity of modern cyber attacks have rendered manual triage mathematically impossible.
By adopting Agentic AI, organizations can reclaim their SOC. Instead of drowning in noise, analysts can return to what they do best: proactive threat hunting and strategic defense, leaving the repetitive tactical warfare to the machines.