NINA AI: Agents for 
Cybersecurity Teams

Close the gap with AI-accelerated adversaries. NINA reasons over your environment to discover, validate and reduce exposure. With your analysts governing every action.

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Trusted by Pioneers in Prevention

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Zynap’s Multi-Agent Engine

Context-aware agents that partner with you to reduce hours of manual work, driving efficiency, scalability, and stronger security operations.

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Outpace AI-Driven Attacks

Cut MTTD and MTTR, and drive down MTRER, the metric for how fast exploitable risk gets reduced, before AI-accelerated attacks have time to land.

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Governed Autonomy

Use ready-to-go agents built by our security experts, or design your own, defining identity, instructions, and tools. Agents reason autonomously inside your guardrails, and every step is traceable, reversible and auditable.

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Maintain your Data Privacy

Use any model from any provider. Different agents can run different models, pick the best for each task. New models arrive automatically, with zero markup on AI costs.

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Proprietary Intelligence

Replace generic AI with Zynap's proprietary intel: real malware, adversary TTPs & cybercrime activity. Citable answers, validated workflows, every action grounded in your environment.

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Inside NINA: Where Intel becomes Action

NINA investigates threats, automate and fix security workflows, pull live threat intel, score vulnerabilities, and act in real time with full context of your environment.

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Context-Aware Across the Platform

NINA works everywhere in Zynap, not just the workflow builder. It answers in the context of the window you have open, so you never have to explain where you are.

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Attack Surface Intelligence

Ask what threatens you and the answer comes from your own internet-facing assets. NINA crosses the vulnerabilities on them against active exploitation and the actors behind it. This is your environment and not a generic feed.

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Live Threat Intelligence

Query threat actors, malware, CVEs, MITRE ATT&CK and credential exposure in plain language, and composite risk scoring tells you what to fix first.

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Conversational Workflows

Describe what you need and NINA designs, builds, runs and monitors the complete automation. It draws you the diagram before you confirm, then runs on its own, and you choose when to step in.

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Workflow Troubleshooting

When a workflow fails, NINA diagnoses the cause, applies a fix and re-runs it to see whether the fix held, all in one conversation.

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Cited Answers

Every answer is grounded in Zynap's intelligence and your own data. It cites real sources that open inside the platform so you never get a fabricated reference.

Behind every answer and every action, NINA runs a team of specialists. There's one for security knowledge, one for workflow design, one for troubleshooting and one for threat intelligence.

They're always running in the background, and you never configure or manage them. NINA picks the right one for whatever you're working on.

zynap nina bulletAI Agents for Cybersecurity Teams

Agents that Outpace Modern Threats

A growing set of specialized cybersecurity agents that live across the platform, ready to use, or fully customizable with your own models and tools. AI that fits the way your team works.

Custom agents

Build AI agents with the LLM and MCP tool of your choice to reason autonomously and execute multi-step tasks across your workflows.

Autonomous Reasoning

Custom Agents reason in a loop: they read data, choose tools, execute, evaluate results, and iterate until the task is done. Watch every step live in the Thought Process panel.

Agent-to-Agent Chaining

Build multi-agent pipelines where specialized agents hand off to each other. Each agent in the chain has its own model, tools, and instructions

Workflow-Native Architecture

Inside any workflow, Custom Agents run as nodes sharing context with the rest of the canvas. Files, scanners, and integrations are automatically available to your agent.

Connect Any Tool via MCP

Give agents access to any external tool through the Model Context Protocol. Connect public services or your own infrastructure, with tools auto-discovered on connection.

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Ready-to-use agents

Our Automation Agents work out of the box inside any workflow, with zero setup and no prompt engineering required.

Data Transformation Agent

Generates code from natural language to transform, normalize, and enrich large telemetry volumes, enabling seamless integrations and intelligence.

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Scripting Agent

Transforms tasks into operational scripts, generating and running optimized code instantly, simplifying coding, troubleshooting, and automation.

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Summarize Agent

Condenses validated, context-relevant content into clear, actionable summaries, removing noise and highlighting key insights.

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Info Screening Agent

Filters and classifies documents by context, extracting relevant insights and removing noise from keyword-based matches.

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Our Solutions

Use Cases

Threat Intelligence

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Transform external intel into immediate action for clients.

Offensive Security

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Enhance client defenses with safe, AI-powered adversary simulations enriched by real threat intelligence context.

Security Operations

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Go beyond basic detection and response to deliver faster, more consistent, and fully contextualized protection.

Capabilities

Automate Your Cybersecurity Lifecycle

Threat Intelligence and Data Sources

From TTPs to credentials, act instantly with correlated, contextual intelligence.

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Automation and Workflows

Build workflows fast with low-code tools, AI agents, and a collaborative canvas.

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Credentials Intelligence

Validate credentials, spot true exploits, and act with instant threat context.

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Close your exposure window and stay ahead

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Frequently asked questions

What are examples of AI agents in cybersecurity?

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.

How do AI agents improve security operations (SOC) efficiency?

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 run under governed automation. Analysts set the guardrails, agents act inside them, and every action is confidence-scored, traceable and reversible. The automation removes the toil, so you keep control of what executes.

Can AI agents be used by MSSPs?

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.

How much control do I keep over what agents execute?

You decide. Agents in Zynap are fully customizable, and customers define the guardrails per workflow based on its risk profile. Some agents run fully autonomously, others require approval at specific decision points before anything executes.

The governance is the same either way. Zynap surfaces the agent’s reasoning and supporting context, including confidence indicators, so an action can be approved, adjusted or overridden, and every executed action is traceable, auditable and reversible. In high-stakes environments, that governance is what makes autonomy safe to switch on.

How do AI agents handle data privacy and sensitive security data?

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.