NINA AI: Agents for 
Cybersecurity Teams

Common examples of AI agents in cybersecurity include: threat enrichment agents that automatically gather context around indicators of compromise (IOCs); OSINT investigation agents that scrape and correlate open-source data about threat actors; scripting agents that write and execute custom detection rules; and response agents that draft incident reports or trigger containment actions.

Zynap offers all of these as pre-built agent types within its NINA AI, covering automation, investigation, and backend integration workflows.

AI agents improve SOC efficiency by eliminating the manual, repetitive work that consumes analyst time: alert triage, log correlation, enrichment lookups, and report writing. By automating these tasks, they directly reduce Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR), while freeing up analyst time for more complex tasks.

Zynap’s agents are designed with a human-in-the-loop model, so analysts receive confidence-scored outputs with clear approval gates rather than fully autonomous decisions, maintaining oversight while removing toil.

Yes. AI agents are well-suited to Managed Security Service Providers (MSSPs) because they enable analysts to handle a higher volume of client environments.

Zynap’s platform is purpose-built for MSSP use cases, supporting multi-tenant workflows, integration with existing SIEM and SOAR tools, and agent-driven automation that can run across multiple client environments simultaneously.

This allows MSSPs to deliver faster response times while controlling operational costs.

A human-in-the-loop AI agent is one that incorporates defined checkpoints where a human reviews and approves the agent's findings or proposed actions before they are executed. This design is particularly relevant in high-stakes environments like cybersecurity, where a fully autonomous action could have significant operational consequences if the agent's reasoning was based on incorrect or incomplete data.

In Zynap, agents are fully customizable: customers decide whether (and where) to introduce human checkpoints based on the risk profile and operational requirements of each workflow. Some agents can be configured to run fully autonomously, while others may include one or more human approval gates at critical decision points. When checkpoints are defined, Zynap surfaces the agent's reasoning and supporting context, including confidence indicators where applicable, so analysts can approve, adjust, or override the proposed action with full visibility.

Enterprise-grade AI agent platforms address data privacy by supporting on-premises LLM deployment, which means sensitive data never leaves the organization’s infrastructure. Zynap’s platform is designed with this requirement, offering encrypted API communication and governance frameworks that ensure compliance with enterprise data handling policies.

This is especially relevant for MSSPs and regulated industries where data residency requirements prohibit sending information to third-party cloud LLMs.