The security operations center is undergoing a fundamental shift. Organizations are adopting AI-powered SOC platforms at an accelerating pace, with the market expected to grow from $25.35 billion in 2024 to over $93.75 billion by 2030. This rapid expansion reflects a real problem: AI-powered phishing attacks have surged 1,265% since 2022, with attackers now automating reconnaissance, crafting adaptive phishing campaigns, and executing lateral movement faster than human analysts can respond.
AI SOC tools promise to solve alert fatigue, automate investigation workflows, and reduce mean time to respond. These are legitimate benefits. The market momentum is justified. But the effectiveness of these tools depends entirely on a prior assumption: that the underlying detection systems surfaced the attack in the first place.
The response to AI-powered attacks
The numbers tell a clear story. Phishing emails generated by AI increased 1,265% since 2022, and organizations worldwide are experiencing sophisticated attacks at unprecedented scale. Attackers are using large language models to automate vulnerability scanning, generate polymorphic malware, and conduct reconnaissance at machine speed.
The defender response has been equally swift. According to Omdia research, 39% of early adopters deploy agentic AI primarily for reduced costs and increased productivity. The technology promises continuous learning, adaptive decision making, and contextual reasoning that traditional SOAR platforms cannot provide. AI SOC platforms now automatically triage alerts, correlate events across disparate tools, and generate investigation summaries that would have taken analysts hours to compile manually.
This is valuable automation. Security teams are overwhelmed. Alert fatigue remains a critical challenge, with organizations receiving thousands of alerts daily, most of which turn out to be false positives. AI-driven alert triage helps analysts focus on genuine threats rather than noise. The problem emerges when you examine what feeds these AI systems.
The upstream dependency problem
AI SOC platforms sit downstream of existing detection infrastructure. They process alerts from SIEM platforms, endpoint detection tools, network monitoring systems, and threat intelligence feeds. The AI layer provides reasoning over this data: correlating events, prioritizing incidents, automating initial response steps. But the quality of that reasoning is limited by the quality of the input.
Traditional detection systems rely on signatures, rules, and behavioral baselines. SIEM platforms correlate known indicators. NDR tools inspect packets for malicious patterns. UEBA systems flag deviations from historical user behavior. These approaches work when attackers trigger known signatures or deviate from baselines. They fail when attackers operate within normal parameters.
Stealthy attacks deliberately avoid triggering traditional detection. Lateral movement uses legitimate protocols and approved services. Reconnaissance respects rate limits and mimics operational traffic patterns. Data exfiltration occurs through standard channels at volumes that fall within expected variance. Individual flows appear normal to systems trained to detect anomalies or match signatures. The aggregate behavior reveals malicious intent, but only if you are looking at the right level of abstraction.
When the upstream detection layer misses the attack, the downstream AI reasoning layer has nothing to process. You get sophisticated automation operating on an incomplete view of what is actually happening in the environment. The AI might correlate the few alerts that did fire, generate a summary, and determine no action is needed. The attack continues undetected.
AI reasoning applied to the right detection signals
The solution is not to abandon AI-powered SOC automation. The solution is to ensure the detection layer surfaces the attacks that traditional systems miss. This requires a different approach to detection itself.
DeepTempo operates on behavioral timelines of network flows between endpoints. Each timeline is a structured representation of how two systems communicated over a bounded time window. The foundation model learns what operational patterns structurally look like: backup jobs, heartbeat checks, service calls, database queries. It also learns what malicious patterns structurally look like: reconnaissance probing sequences, credential abuse, lateral movement paths.
Detection happens at this behavioral level. Each timeline is evaluated independently for malicious intent based on its structural signature. Attackers can make individual flows appear normal by using legitimate protocols, staying within rate limits, and avoiding payload inspection triggers. They cannot make the communication structure normal while accomplishing their objectives. The structure itself reveals intent.
This is not anomaly detection. It is intent-based detection. Traditional anomaly detection measures deviation from baseline and fails when attackers stay within established patterns. Intent detection recognizes the structural signatures of malicious behavior independent of whether they deviate from operational norms. A reconnaissance pattern has a distinctive structure. A lateral movement sequence has a distinctive structure. These structures exist whether or not the activity deviates from what the environment considers normal.
When the detection layer surfaces threats based on malicious intent rather than signature matches or baseline deviations, the downstream AI reasoning layer has meaningful data to process. The AI can correlate malicious behaviors, understand the sequence of attacker actions, and automate response workflows based on genuine threats rather than false positives or missed attacks.
Building toward AI-powered investigation and response
The path forward combines intent-based detection with AI-powered investigation capabilities. The right architecture integrates LLM reasoning with vector similarity search, automated alert enrichment, and natural language query interfaces for threat investigation. This approach demonstrates how reasoning should layer onto high-fidelity detection.
The design principle is straightforward: detect threats that traditional systems miss, then apply AI reasoning to those detections. The reasoning layer correlates related activities, maps them to MITRE ATT&CK tactics, retrieves similar historical patterns from vector storage, and generates contextual explanations of what the attacker attempted to accomplish. Analysts can query the system in natural language to understand threat scope, identify affected assets, and determine appropriate response actions.
This approach inverts the typical AI SOC workflow. Instead of applying sophisticated reasoning to an incomplete alert stream, it applies reasoning to a complete detection stream that includes the stealthy attacks traditional tools miss. The AI layer becomes genuinely useful because it operates on high-fidelity input.
What this means for security operations
The AI SOC market will continue expanding. Organizations need automation to handle alert volume and analyst shortages. But the effectiveness of that automation depends on the quality of the underlying detection layer. Sophisticated alert triage applied to a system that misses stealthy attacks produces efficient processing of the wrong information.
Detection engineering teams should evaluate their upstream detection capabilities before layering AI reasoning on top. Ask whether your current detection stack surfaces reconnaissance that uses legitimate services. Ask whether you catch lateral movement that stays within approved protocols. Ask whether you identify data exfiltration that falls within normal volume ranges. If the answer is no, AI-powered alert triage will not solve the problem.
The combination of intent-based detection and AI-powered investigation represents a more robust approach. Detect the attacks first. Then apply reasoning to understand scope, correlate events, and automate response. This sequence produces operational value because the AI works with complete information rather than the subset of attacks that happen to trigger traditional detection rules.
Closing note
AI-powered attacks are accelerating. Defenders need AI-powered response. But response automation only works when detection automation surfaces the threats in the first place. The AI SOC market is solving the right problem with the wrong starting point. Organizations that layer reasoning over traditional detection will automate the processing of an incomplete threat picture. Organizations that start with intent-based detection and then apply AI reasoning will automate response to the full scope of attacker activity.
DeepTempo detects malicious intent at the network communication level, catching the stealthy attacks that signature matching and anomaly detection miss. The architecture that scales: detect what others miss, then reason over complete information.
Get in touch to run a 30-day risk-free assessment in your environment. DeepTempo will analyze your existing data to identify threats that are active. Catch threats that your existing NDRs and SIEMs might be missing!
MITRE: Command and Control, Reconnaissance, Lateral Movement
Related reading:
- 2026 is the year of AI-powered attacks. Here's the evidence
- How AI-powered security actually works: Beyond the hype
- From reactive to predictive: Building tomorrow's security operations center
