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In 2025, AI for cybersecurity transitioned from being an interesting concept to an urgent reality.
Anthropic's threat intelligence team discovered that a Chinese state-sponsored group had weaponized AI agents to orchestrate a cyber espionage campaign targeting roughly 30 global organizations—including Fortune 500 tech companies, financial institutions, and government agencies.
The threat actors configured Claude to execute 80-90% of the attack independently. Reconnaissance. Credential harvesting. Lateral movement. Data exfiltration. The AI handled it all, with humans stepping in only to authorize critical escalation decisions.
For most security leaders, this raised a sharp question: If attackers can weaponize AI to operate at that level of sophistication and speed, how can we keep up?
67% of all daily alerts are left unaddressed, overwhelming SOC Analysts and creating gaps in an organization's defense. The remainder accumulate in backlogs, get deprioritized, or slip through entirely. More troubling, 60 percent of CISOs in that same survey acknowledged missing alerts that later became confirmed breaches.
Your analysts can't move fast enough. The mean time to investigate an alert currently sits around 70 minutes. That might sound reasonable until you compare it to attacker speed. A phishing campaign succeeds within an hour. Lateral movement across systems happens in minutes once credentials are compromised. By the time your SOC team finishes initial investigation on the first alert, attackers have already escalated access, disabled logging, and begun exfiltration.
SOAR platforms promised to solve this. They've been the SOC automation standard for a decade. But Gartner deprecated SOAR in 2025 because it no longer works in the face of new AI-powered attacks. SOAR requires you to write playbooks for known attack patterns, but the moment an attack deviates from the script, which AI-powered attacks are designed to do, SOAR can’t help. The only action is to escalate everything uncertain to humans, defeating the entire purpose of automation.
That context gap isn't a feature gap. It's why SOAR has reached the end of its useful life and the SOAR alternative - AI SOC Analyst is here!
Gartner placed SOAR in the "Trough of Disillusionment" on its 2025 Hype Cycle for IT Service Management, and in 2025, went further: the technology is now expected to be obsolete before reaching productive maturity.
The core limitation stems from the design philosophy. SOAR requires you to script responses in advance. You write playbooks for credential compromise scenarios. You define decision trees for malware detection. You map out investigation workflows for lateral movement patterns. The entire system assumes you know what you're looking for and how you'll respond before the attack happens.
That worked reasonably well when threat actors followed predictable patterns. It breaks down entirely when facing AI-powered attacks that adapt in real time, modify their techniques mid-campaign, and target organizational vulnerabilities your playbooks never anticipated. The moment an attack deviates from your predefined script, SOAR escalates to human analysts. For most organizations, SOAR became another tool that required constant attention rather than the force multiplier it had promised to be.
SOAR consistently fails at what industry analysts call the "last mile problem." They can collect alerts. They can pull logs. They can execute predefined actions. But they cannot synthesize business context with security signals to make autonomous decisions.
A common situation that occurs thousands of times each day in enterprise Security Operations Centers (SOCs): your identity monitoring tool flags an executive logging in to the corporate VPN from Moscow at 2 AM Eastern Time. SOAR can tell you the login happened. It can retrieve the IP address, check it against threat intelligence databases, pull the user's recent authentication history, and even suspend the account based on geographic anomaly rules.
What SOAR cannot tell you is whether this executive is legitimately traveling for the company's Eastern European expansion, whether their team is working with Russian contractors this quarter, or if this pattern matches the executive's typical behavior during international business trips. That contextual understanding, the ability to distinguish normal from anomalous within your specific business operations, requires human judgment every single time.
Without effective automation, the work of processing and responding to security alerts falls back to the SOC team. With finite analyst capacity, you can investigate 60 percent of alerts thoroughly, or you can examine 100 percent of them superficially. You cannot do both.
In the current SOC team structure, adding depth of coverage requires progressively more expertise, more time per alert, and more expensive talent. Deprioritizing low-severity alerts risks missing reconnaissance activity that precedes major breaches. Shallow investigations miss subtle indicators of compromise that deeper analysis would catch. Time-based coverage gaps create predictable windows for attackers' operations. Trying to cover all these just results in alert fatigue at scale. Analysts become desensitized to repetitive alerts, knowing they cannot possibly investigate every signal with appropriate thoroughness.
This is not a hiring problem you can solve by adding more L1 analysts. It's a structural problem and requires tranforming your SOC Analyst to AI SOC Analysts
Simbian's AI SOC agent represents a fundamental shift from SOAR's reactive and static automation to a proactive, autonomous response. Unlike SOAR playbooks that require analysts to choose an investigation path, AI SOC agents autonomously triage, investigate, and correlate alerts using real-time reasoning about your specific threat landscape.
The defining capability is context-aware decision making. While SOAR cannot answer whether John's Moscow login is legitimate, AI SOC agents synthesize business context—travel schedules, team composition, normal behavior baselines—with security signals to make autonomous judgments. They pull contextual data from identity systems, asset inventories, threat intelligence, and organizational knowledge bases without waiting for analyst intervention.
This autonomous investigation occurs at machine speed 24/7. Between the time a phishing email lands and your team arrives Monday morning, AI SOC agents have already triaged hundreds of alerts, investigated commonalities, identified campaigns, and contained threats. They don't experience analyst fatigue, coverage gaps during off-hours, or decision paralysis from alert overload.
The result is fundamentally different from assisted analysis. Co-pilots speed up analysts by 50 percent. AI SOC agents eliminate the need for analyst involvement in 90 percent of alert processing, freeing your team to focus on strategic threat hunting and detection engineering. They operate independently while remaining coachable- analysts provide feedback that improves future investigations without requiring code changes.
The Anthropic revelation confirms that we have entered an era of AI-on-AI warfare. When adversaries use AI agents to execute attacks at machine speed, reliance on static SOAR playbooks and finite human attention is a losing strategy.
To survive the threat landscape of 2026, security leaders must bridge the context gap. By deploying Simbian’s AI SOC Agent, organizations can move past the limitations of legacy automation. This shift isn't just about efficiency; it's about efficacy—ensuring 100% of alerts are investigated with full organizational context, 24/7. This frees your human experts to focus on the complex security threats that are beyond the reach of AI.
The adversaries have already adopted AI agents. It is time your defense did the same. Book a demo today to see how Simbian can autonomously resolve over 90% of your alerts.
