Beyond the Alert Queue: How Agentic SecOps Helps Security Teams Scale
Author: Jack Hancox
Release Date: 18/08/2026
Security operations teams are facing a problem that is becoming increasingly difficult to solve with traditional approaches: the volume of security work is growing faster than the number of people available to handle it. Modern enterprises already operate dozens of security tools across their environments, generating thousands of alerts, events and investigations. At the same time, threat actors are using AI to accelerate attacks, reducing the time defenders have to detect and respond.
The answer isn't simply to hire more analysts or send every alert to an AI model. Instead, security teams need to rethink how work gets done.This is where agentic SecOps comes in, also known as AI-SOC and Agentic SOC.
The security operations scale problem
Security teams have always had to balance increasing alert volumes with limited resources. As organisations add more endpoints, cloud services, applications and security controls, the amount of data requiring attention continues to grow.
The result is a familiar cycle:
- More security tools generate more alerts.
- More alerts require more investigation.
- Manual response processes slow down remediation.
- Security teams prioritise critical alerts while lower-severity activity receives less attention.
- Analysts spend increasing amounts of time on repetitive tasks rather than higher-value investigations.
The problem is that threats are not waiting for security teams to catch up. AI is helping attackers automate reconnaissance, identify vulnerabilities and accelerate exploitation. The window between vulnerability discovery and exploitation can therefore shrink dramatically. Security operations need to respond at machine speed and scale, while still keeping humans accountable for important decisions.
Why simply adding AI isn't enough
As AI-powered SOC solutions have emerged, organisations have generally been presented with three approaches.
1. Buy a black-box AI tool
Generic AI solutions can provide impressive demonstrations, but they may have limited understanding of an organisation's specific environment, processes and security requirements. They can also be restricted to narrow use cases, making it difficult to extend automation as requirements evolve.
2. Add AI to an existing SOAR platform
Another approach is to bolt AI onto traditional security orchestration and automation. This can help, but the underlying platform still relies heavily on manually configured playbooks, integrations and processes. Teams may need to complete significant engineering work before they can realise the benefits of AI.
3. Build your own AI agents
Building internally provides greater control, but it introduces its own challenges. Organisations need specialist AI and engineering expertise, while also managing credentials, permissions, infrastructure, model costs and the security risks associated with autonomous systems. None of these approaches fundamentally change one of the biggest challenges facing SOC teams: You still don't have more operators.
Agentic SecOps: automation where it makes sense
A more effective approach is to combine deterministic automation with AI reasoning.
Rather than asking an AI model to process everything, security teams can use automation for predictable tasks and reserve AI for situations where reasoning is genuinely required.
The principle is simple:
Deterministic before. Agentic in the middle. Deterministic after.
This model creates a controlled operating pattern:
- Fixed automation handles predictable preparation and data gathering.
- AI agents analyse information and reason within defined boundaries.
- Humans remain accountable at key decision points.
- Deterministic automation executes approved response actions.
- Governed integrations provide access, permissions and complete auditability.
This allows organisations to introduce AI without simply handing an autonomous system unrestricted access to their environment.
From seeing alerts to understanding them
A useful way to think about agentic SecOps is through four stages (the SUDA loop): See, Understand, Decide and Act.
See: Bring everything into view
Effective security operations start with visibility. Alerts can originate from SIEM and EDR platforms, cloud environments, applications, service desks and many other systems. The ability to connect these sources is essential. With more than 30,000 API actions and support for hundreds of vendors, BlinkOps enables organisations to bring data and actions from across their security ecosystem into a single automation layer. The goal is to avoid creating blind spots simply because a particular system isn't connected to the SOC.
Understand: Build the context
An alert on its own rarely tells the complete story. Before making a decision, the security team needs to understand the users, devices, IP addresses, indicators of compromise, threat intelligence and other relevant information surrounding the event. Automation can gather and enrich this context quickly. It can also identify duplicate alerts and correlate related activity, potentially turning hundreds of individual alerts into a single investigation. This is an important distinction. AI doesn't need to reason about 500 duplicate alerts if automation can establish that they belong to the same incident first.
Decide: Use AI where reasoning matters
Once the relevant information has been collected and structured, AI can be used where it adds the most value. An agent can assess the available evidence, determine the likely severity of an incident, identify affected systems and recommend the appropriate next step. This is where frontier AI models can be particularly useful, not for every alert, but for complex cases where human-like reasoning is required. Providing the model with the right context is critical. Without sufficient information, AI can produce inaccurate conclusions or hallucinations. By gathering and structuring the relevant evidence first, organisations can give AI a much stronger foundation for decision-making.
Act: Automate the response safely
The final stage is response and remediation. This is where governance becomes particularly important. Security responses are rarely identical from one organisation to another. One organisation may automatically disable a compromised account, while another may require a change request or approval from another team. Agentic SecOps should therefore not mean allowing an AI agent to take unrestricted action. Instead, predefined workflows can execute approved responses, with human approval required wherever organisational policy demands it. This creates a balance between speed and control.
Why you shouldn't send every alert to an LLM
One of the biggest misconceptions around AI-powered security operations is that every alert should be sent directly to a large language model (LLM). In reality, this can introduce unnecessary cost, latency and complexity. Security environments contain large numbers of duplicate alerts, known patterns and predictable processes that can be handled using conventional automation.
For example, deterministic automation can:
- Parse incoming alerts
- Extract indicators and entities
- Enrich data from security tools
- Check reputation information
- Correlate related events
- Deduplicate alerts
- Apply predefined rules
- Execute approved workflows
These tasks can happen quickly and consistently.
AI can then be reserved for the difficult cases where interpretation and reasoning are required.
The result is a hybrid model that combines the reliability and speed of deterministic automation with the flexibility of AI.
Human oversight remains essential
Agentic SecOps isn't about replacing security professionals. It's about giving them leverage. The objective should be for analysts to spend less time manually processing repetitive alerts and more time investigating complex threats, improving controls and making strategic decisions. Importantly, autonomy should be earned rather than granted by default. For straightforward, high-confidence scenarios, an organisation may choose to allow an automated workflow to close an alert or perform a predefined action. For more complex or higher-risk situations, the system can escalate the case to a human analyst. This creates different levels of autonomy depending on confidence, risk and organisational policy.
A "glass box" approach to AI security operations
For organisations adopting agentic AI, transparency is just as important as automation.
Security teams need to understand:
- What information the agent received
- What enrichment was performed
- Which questions the agent considered
- Why it reached a particular conclusion
- Which workflows were executed
- What actions were taken
- Where human approval was required
A transparent approach makes it easier for security teams to validate decisions, investigate unexpected outcomes and demonstrate compliance.It also helps organisations improve their automation over time.
The importance of the feedback loop
Agentic SecOps shouldn't be a set-and-forget technology.
Every investigation can provide information that improves the security operation:
- If an alert is repeatedly determined to be a false positive, the organisation may be able to improve its detection logic.
- If an investigation repeatedly lacks a particular piece of context, the team can improve its integrations or data collection.
- If a response consistently requires the same manual intervention, that process may be a candidate for automation.
This creates a continuous feedback loop:
Detect → Investigate → Decide → Respond → Learn → Improve
Over time, more of the predictable workload can be automated, while human analysts remain focused on the areas where their expertise has the greatest impact.
From plug-and-play to fully customised
One of the benefits of a modern agentic SecOps platform is that organisations don't necessarily have to choose between rapid deployment and customisation. Teams can start with pre-built capabilities to process common security use cases quickly, then customise workflows, questions, agents and response actions as their requirements mature.
For example, agents can be configured with:
- Defined roles and responsibilities
- Reasoning constraints
- Specific abilities and permitted actions
- Approved workflows
- Organisational policies and procedures
- Internal security knowledge
- Environment-specific context
This allows organisations to move from "AI out of the box" to "AI built around our security operation."
Where Somerford fits in
Technology is only one part of the equation. At Somerford Associates, we believe successful security transformation comes down to three things: people, technology and experience. Our team works with enterprise organisations facing complex security and operational challenges, helping them identify where automation can deliver meaningful value and how emerging technologies can be integrated into existing environments. With expertise across security operations, automation and leading security technologies, we can support organisations beyond the initial technology deployment — from professional services and implementation through to enablement, workshops and ongoing support. The goal isn't simply to add another platform to the SOC. It's to help organisations build a security operation that can do more, respond faster and scale without relying solely on additional headcount.
The future of SecOps is about execution
Security teams don't have a shortage of alerts. They have a shortage of time and capacity. As threats continue to accelerate, organisations need to rethink how their security operations scale. Agentic SecOps provides a way to combine automation, AI reasoning and human expertise, allowing machines to handle repetitive execution while people remain responsible for the decisions that matter. The most effective approach isn't AI everywhere. It's AI where it counts, automation where it can, and humans where they should. That is how security teams can move beyond simply managing the alert queue and start building a SOC capable of operating at the speed and scale of modern threats.