Netskope: Multi-Agentic AI Solution for Enhanced Security Operations
Crest Data developed a sophisticated Multi-Agentic AI System for Netskope, leveraging AWS Bedrock to revolutionize security operations
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Client Overview
Netskope is a leader in Security Service Edge (SSE) and Zero Trust solutions, helping organizations secure with cloud-native security. Their platform provides visibility, real-time data and threat protection, and policy enforcement across cloud, web, and private applications.
Key Challenges
Security teams face overwhelming volumes of alerts across multiple security tools, leading to significant operational challenges:
Overwhelming Alert Volume: Security Operations Centers handling 10,000+ daily alerts across 100+ locations
Disconnected Systems: Critical security information siloed across Netskope, Okta, Crowdstrike, and other platforms
Resource Constraints: Understaffed SOC teams unable to investigate all potential threats
Manual Workflows: Time-consuming processes for alert correlation and investigation
Reactive Response: Limited ability to proactively identify emerging threat patterns
These challenges required a new approach beyond traditional security automation—one that could intelligently coordinate multiple capabilities to function as an autonomous security operations system.
Crest Data's Multi-Agentic AI Solution
Crest Data developed a sophisticated Multi-Agentic AI System for Netskope, leveraging AWS Bedrock to revolutionize security operations:
Specialized Agent Architecture
The solution deployed multiple collaborative AI agents, each with specific roles:
Alert Triage Agent: Autonomously evaluates and prioritizes security alerts
Context Gathering Agent: Collects relevant information across security systems
Correlation Agent: Identifies relationships between seemingly disparate events
Response Recommendation Agent: Suggests appropriate actions based on threat analysis
Documentation Agent: Creates comprehensive reports and maintains investigation records
Agent Orchestration System
Sophisticated coordination layer allowing agents to work together on complex investigations
Dynamic task allocation based on alert severity and system load
Reasoning engine to determine optimal investigation paths
Human-in-the-loop integration points for critical decisions
Multi-Agent Implementation Approach
Agent Design Phase: Defining specialized agent roles, capabilities, and collaboration patterns
Coordination Framework: Building the orchestration system for inter-agent communication
Tool Integration: Implementing solution for each security system connection
Reasoning Development: Training specialized reasoning capabilities for security analysis
Workflow Automation: Creating autonomous investigation sequences across agent teams
Human Oversight Integration: Designing effective human-AI collaboration interfaces
Technologies Leveraged
AWS Bedrock
LangGraph
Anthropic Claude
Streamlit
Kubernetes
Ready to transform your operations with AI Agents? Contact Crest Data to learn how our AI Agent development expertise can revolutionize your business processes.