The CardinalOps Agentic Fleet
Equip your SOC for success in the AI era, with agents flying alongside your team to optimize the detection lifecycle.
Reinvent Your SOC for the AI Era
Detection engineering has absorbed the strain of growing telemetry, evolving adversaries, and
sprawling tools, and the AI era is exacerbating the pressure. It’s time for a new model to take flight.
A Purpose-Built, Coordinated System, Powered by Cardinal AI
The Detection Engineering Foundation
The Fleet’s actions are driven by the Foundation: a knowledge layer that combines extensive detection expertise with customer-centric context and threat context.
Detection Expertise
Resilient detection logic, proven adversary mapping tactics, and SIEM-specific knowledge from decades of elite detection engineer experience.
Customer Context
The customer’s unique detection methodologies and tuning preferences, reinforced through feedback loops so the Fleet adapts to how your SOC operates.
Threat Context
Context on the evolving threat landscape-new APTs, adversary tactics, and threat vectors–and how to quickly translate intel into threat-informed defenses.
Charting a New Way Forward for Detection Engineering
Scale Capacity of Your Current Team
Automate manual operational tasks, enabling your lean team to operate and succeed at enterprise scale.
Reduce Noise and Improve Response
Increase signal quality and eliminate noise, so your SOC can focus on threats that matter most.
Automate SOC Feedback Loops
Incorporate triage and investigation outcomes into automated workflows that continuously refine detections.
Streamline Lifecycle Management
Reduce costs and offload operational overhead with AI-driven maintenance of large rule sets.
Meet Your Agentic Fleet Operators
Frequently Asked Questions
Our answers to frequent questions:
Agentic Detection Engineering is the process of using agentic AI to automate the manual, time-consuming aspects of detection lifecycle management. Agents operate autonomously using multi-step planning and execution workflows to identify coverage gaps, develop new rules, tune existing rules, and operationalize threat intelligence and threat hunting findings into new detection coverage. With Agentic Detection Engineering, AI agents can observe the effects of their actions and incorporate feedback from human detection engineers to continuously learn and improve their approach to detection engineering.
In general, the agents within the Fleet are embedded directly into existing CardinalOps platform UI components. The Fleet’s findings for optimizing your detection environment are delivered as “Agentic Recommendations” under the Cards. All of the Fleet’s actions are logged and accessible via the Dashboard under “Agentic Activities”rnrnWingman is accessible via a dedicated tab in the side menu, allowing you to engage in a familiar generative AI chat user experience. You can prompt Wingman to generate new rules, survey existing coverage, review tools in your environment, and much more.
CardinalOps does not train our models on any specific sensitive customer data. Insights that our models generate for optimizing detection workflows will only ever use aggregated, anonymized data, ensuring sensitive customer data is never exposed. We also have enterprise agreements with each underlying LLM vendor that explicitly prohibit any use of our customers’ data for their own platforms, further ensuring customer data privacy.
Collaboration workflows largely depend on how many agents are included in the customer’s license package. For customers that don’t have all agents in the Fleet, agentic workflows will be limited in scope to accomplish tasks aligned with the agents they’ve purchased.rnrnFor customers with the entire Fleet, all agents will collaborate as necessary to complete their tasks. For example, if a user prompts Wingman to review coverage for a specific APT and create a relevant detection, Wingman may reference findings from Threat Interpreter and engage the False Positive Terminator to provide proactive tuning recommendations.
No. At CardinalOps, our philosophy is that AI should augment–never replace–human detection engineers, preferring human-in-the-loop or human-on-the-loop AI workflows that allow your end users to review and approve the Fleet’s recommendations before deploying changes. While the agents in the Fleet may act autonomously to complete discrete tasks, they won’t deploy anything that impacts production systems unless given end user approval.
Since the Fleet is embedded into the existing CardinalOps platform, the deployment timeline is the same as any standard platform deployment. It largely depends on how quickly the customer can work through the necessary API integrations with the CardinalOps support team. The Fleet can be operational in a matter of hours.
Qualifying prospects may include the Fleet within the broader scope of a standard proof of value (POV) deployment. Customers can request access to the Fleet for a limited period of time on a trial basis before upgrading to the full, paid version.
Change the Detection Engineering Equation
