Agentic SecOps: AI agents that investigate and respond alongside your AI SecOps
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Agentic SecOps uses AI security agents to triage, investigate, correlate and respond under human supervision. Loonar designs, deploys and supports this operating model across SIEM, MDR, identity, endpoints and incident management. An AI SecOps gives analysts evidence and context while keeping sensitive decisions with people rather than handing them to autonomous tools.
In short
- Automate tasks, not accountability.
- Require evidence before taking action.
- Keep people in charge of critical decisions.
How do you know you need agentic SecOps?
Queues outpace capacity
Repeated alerts consume the same attention as meaningful signals when context is missing. Analysts spend their shift sorting events rather than investigating threats, leaving important cases waiting while the queue continues to expand across disconnected sources.
Staffing limits
Experienced analysts juggle evidence collection, documentation and urgent decisions throughout their shift. Reliance on a few specialists makes coverage fragile and creates inconsistent handovers when another team must take ownership of an active investigation.
Slow investigations
Evidence lives across systems with different timestamps and identifiers. Rebuilding an incident requires manual queries and repeated checks, delaying hypothesis testing and making it harder to explain why a particular containment action is justified.
Inconsistent response
Informal procedures leave approvals, actions and supporting evidence scattered across conversations. After an incident, the team struggles to establish who decided what, whether the action worked and which changes would prevent the same operational confusion.
How does it work?
Automated triage and enrichment
Agents gather authorized context, group related signals and apply agreed classification criteria. Analysts receive supporting evidence and reasoning, with uncertain cases routed for review instead of silently dismissed.
Guided investigation
Investigations assemble timelines, make hypotheses explicit and highlight missing evidence. References to source events allow analysts to challenge conclusions and continue work without reconstructing the previous shift's reasoning.
SOAR response playbooks
Playbooks run approved actions and request human authorization for sensitive changes. Least privilege, execution logs and recovery procedures constrain the consequences of an incorrect recommendation or failed action.
Cross-domain correlation
Identity, endpoint, network and cloud signals provide connected investigative context. Analysts can distinguish unrelated events from suspicious sequences without assuming that a correlation alone proves an attack occurred.
Incident mobilization
Workflows engage IT and business owners, capture decisions and track dependencies. Clear communication links technical findings to containment, continuity and recovery choices with explicit ownership throughout the incident.
Agent and LLM protection
AIDR monitors potential misuse of enterprise agents and language models. Investigation considers data access, tool calls and behavioral deviations within available telemetry, with defined escalation and response permissions.
How does Loonar deliver?
Operational assessment
We review queues, sources, permissions and decision points across the AI SecOps. Repetitive work and operational risks shape the initial use cases, with clear evidence requirements and automation boundaries.
Control design
Loonar designs playbooks, escalation criteria and approval paths with accountable owners. We establish least privilege, data protection and stop conditions before expanding what an agent can do independently.
Deployment and integrations
We connect authorized sources and incident workflows, checking identity context and audit records. Controlled scenarios test expected behavior and integration failures before enabling responses that can affect production.
Analyst training
We train analysts to review hypotheses, challenge recommendations and take control. Exercises cover incomplete evidence, unavailable sources and malicious content intended to steer an agent toward unsafe actions.
Operations and improvement
We review decisions, exceptions and evidence quality with the team. Lessons from incidents inform playbook changes, with version history and explicit criteria for expanding or reducing agent permissions.
What results do we measure?
- MTTD
- Time from an agreed observable event boundary to detection.
- MTTR
- Time from the agreed incident milestone to the defined response or resolution.
- Automatically triaged alerts
- Alert volume processed through automation under reviewed quality criteria.
- Analyst hours freed
- Estimated manual effort avoided against a measured baseline.
- Escalated false positives
- Escalated alerts subsequently classified as false positives through human review.
How do a traditional AI SecOps, SOAR and agentic SecOps compare?
These approaches can work together when ownership and limits are explicit.
| Aspect | Traditional AI SecOps | SOAR | Agentic SecOps |
|---|---|---|---|
| Triage | Analyst-led | Predefined rules | Context-aware agents |
| Investigation | Manual queries | Structured sequences | Reviewable hypotheses |
| Response | Human execution | Deterministic playbooks | Bounded, approved actions |
| Oversight | Procedures | Rules and permissions | Evidence, permissions and supervision |
Where do we apply it?
Financial services
Investigating suspicious access requires identity, device and transaction context without disrupting essential services. Agents can collect authorized evidence and prepare hypotheses for review. Analysts decide on containment while business owners assess consequences, document exceptions and agree on the conditions for restoring normal access safely.
Retail and e-commerce
During peak trading, account and infrastructure alerts compete with store availability for attention. Contextual triage groups related events and prepares an investigation. Actions that could interrupt sales remain subject to approval, with tested recovery procedures and clear communication between security, operations and business teams.
SaaS
Distributed teams need to investigate access and configuration changes without exposing one customer's data to another context. Playbooks enforce access boundaries and show where evidence originated. Sensitive decisions go to accountable owners, while incident reviews improve escalation rules and permissions as environments and service responsibilities change.
Frequently asked questions
What is agentic SecOps?
Agentic SecOps uses AI agents to carry out linked security tasks within defined boundaries. Unlike a fixed automation sequence, an agent can choose queries and organize hypotheses based on available context. That flexibility does not make its conclusions inherently reliable. Verifiable evidence, restricted permissions and human oversight remain essential when deciding how to investigate and respond.
Does AI replace AI SecOps analysts?
An AI SecOps should support analysts, not remove their accountability. AI security agents help collect, classify and organize evidence while people assess uncertainty, business impact and sensitive actions. Loonar deploys workflows, trains the team and supports ongoing improvement. The operating model must preserve human intervention, including when a source fails or an agent makes an unsuitable recommendation.
How do you stop AI from taking the wrong action?
No design can guarantee that errors never happen. Controls combine least privilege, approved tools, human authorization for critical actions and controlled testing. Teams also need records of inputs, evidence and actions, defenses against malicious instructions embedded in retrieved data, and clear stop and recovery procedures. Greater autonomy should follow operational evaluation rather than assumptions about model quality.
Will it integrate with my existing SIEM?
Integration depends on the interfaces, permissions and access conditions available in your environment. We assess query support, event formats, usage limits and action logging before agreeing on scope. The SIEM can remain the source for detections and historical events. Replacing it is not a prerequisite, but any integration gaps must be identified and addressed in the design.
What is AIDR?
AIDR refers to detection and response for enterprise AI use, including agents and language models. Its scope can cover inappropriate data access, tool misuse and behavior outside agreed policies. Coverage depends on available instrumentation and does not eliminate every AI risk. Effective response still requires accountable owners, investigation criteria and actions that have been explicitly authorized in advance.
Shall we design the next version of your operation?
Tell us the challenge. One conversation with a specialist gives you a clear path, even if it isn't time to buy yet.