FAQ
Straightforward answers for security, IT, risk, compliance, and engineering teams evaluating AI security, AI DLP, Shadow AI governance, and AI-related investigations.
Netra Security is an autonomous AI security platform built for the GenAI era. It helps enterprises discover Shadow AI, govern AI usage, protect sensitive data, monitor AI agent activity, and investigate AI-related data risks. Netra focuses on the data interactions that happen across employees, endpoints, browsers, AI chatbots, AI coding assistants, SaaS applications, cloud storage, and agentic workflows.
AI DLP is data loss prevention designed for how employees and AI agents use generative AI tools. Traditional DLP mainly focuses on files, channels, and policy rules. AI DLP also needs to understand prompts, responses, uploaded files, copied content, AI coding activity, tool calls, account context, and user intent. Netra helps security teams detect and control sensitive data movement across modern AI workflows.
Netra helps identify AI tools used across the organization, including sanctioned and unsanctioned tools. It gives security teams visibility into which AI applications are being accessed, who is using them, which departments are adopting them, and whether usage is happening through browsers, desktop applications, or other endpoint activity. This helps teams move from unknown AI usage to a governed AI inventory.
Yes. Netra is designed to help secure AI coding workflows involving tools such as Cursor, GitHub Copilot, Claude Code, and other AI IDEs or coding assistants. It helps detect when source code, secrets, API keys, or proprietary engineering content may be exposed through AI-assisted development workflows. Security teams can use this visibility for audit, investigation, and policy enforcement.
Netra can help security teams understand AI activity beyond simple web access logs. Depending on configuration and supported channels, Netra can provide visibility into prompts, responses, file uploads, file downloads, tool usage, MCP-related activity, API or function calls, and agent-driven actions. This context helps analysts reconstruct what happened, what data was involved, and whether the activity matched legitimate business intent.
BYOAI happens when employees use personal or unmanaged AI accounts alongside approved enterprise AI tools. Netra helps security teams distinguish corporate AI usage from personal AI usage where account context is available. This allows organizations to enable safe AI adoption while reducing the risk of company data being pasted into personal AI accounts or unmanaged AI environments.
Netra supports AI conversation review when full content audit is enabled for the relevant AI channels. Security teams can review conversation records, prompts, responses, attachments, summaries, detected risk patterns, and related activity details. This helps with incident investigation, compliance review, and understanding whether sensitive data appeared in an AI interaction.
AI Guardrail helps administrators review AI conversations that have been classified by risk level and risk pattern. It provides statistics for critical, high, medium, and low-risk conversations, highlights pending treatment volume, and supports case handling for individual conversations. Analysts can review the reason, topic, summary, risk type, and treatment status before deciding whether to warn a user, escalate a case, or mark a false positive.
Financial services organizations need to protect customer data, employee records, transaction-related information, credentials, internal documents, and regulated data as employees adopt AI tools. Netra helps security and compliance teams monitor AI usage, detect sensitive data exposure, distinguish corporate and personal AI accounts, preserve audit evidence, and investigate risky AI conversations or data movement.
Netra is designed to support granular governance rather than only blanket blocking. Security teams can discover AI tools, review usage patterns, create control policies, apply policies to selected users or groups, allow trusted internal destinations, generate activity logs, and use reminders or approval flows where appropriate. The goal is to help organizations enable AI productivity while reducing data security risk.
Netra combines content detection with user behavior, context, channel information, device scope, activity sequence, and intent analysis. Instead of treating every sensitive data event the same way, Netra helps analysts understand whether an action looks like normal work, accidental oversharing, risky behavior, or potential data exfiltration. This context helps security teams prioritize the alerts that matter.
The Netra AI Activity Graph is the product concept for connecting data, users, devices, AI tools, agents, prompts, responses, files, and actions into one investigation context. It helps security teams trace how sensitive data moves across human and agentic workflows, including where data originated, which AI tool or agent touched it, and what happened next.
Yes. Netra helps security teams investigate AI-related incidents by reconstructing relevant activity, showing conversation details, surfacing sensitive data flow, summarizing long interactions, and linking events to users, departments, tools, accounts, devices, and channels. This gives analysts a clearer evidence trail when reviewing potential data leakage, insider risk, or unsafe AI usage.
Netra is designed for enterprise deployment realities. The company presents both SaaS delivery and privacy-first deployment options. SaaS supports faster time to value, while privacy-first deployment is intended for organizations with stricter data residency, tenant isolation, or regulated environment requirements.
Netra is built for security, IT, risk, compliance, and engineering teams that need to govern AI adoption without stopping productive work. It is especially relevant for organizations where employees use GenAI tools, AI coding assistants, SaaS applications, cloud storage, and sensitive internal data in daily workflows.