Dataiku vs Zscaler

AI building agent that turns a business objective written in plain language into a governed Dataiku project of data pipelines, models, agents and applications rendered as an editable visual workflow.

Centralised control plane inside the Dataiku platform for creating, orchestrating, deploying and tracking AI agents across teams.

Expert-to-Agent engine that converts subject-matter-expert know-how into governed AI agents grounded in enterprise data with structured reasoning and human oversight.

Cross-platform governance product that discovers every AI agent an enterprise is running -- on Microsoft Copilot Studio, Azure Foundry, Salesforce Agentforce, AWS Bedrock, Google Vertex, Databricks, Snowflake Cortex, n8n or Dataiku itself -- measures each agent's business and technical performance, and flags the ones that pose the greatest risk. Announced 2026-09-24; distinct from Dataiku Agent Hub, which creates and operationalizes agents rather than discovering and governing agents built anywhere.

Control layer over the Dataiku LLM Mesh that caps LLM spend, screens prompts and outputs for sensitive or malicious content, and scores model output quality.

Centralised gateway that routes, meters and governs an organisation's connections to multiple LLM providers from inside the Dataiku platform.

Governance layer for employee use of generative AI that discovers shadow AI applications, applies allow, block or coach policies per user, and monitors prompts and responses against more than 100 data loss prevention dictionaries to stop sensitive data leaving through AI tools.

AI security posture management that auto-discovers and classifies AI models, agents and services across Azure AI Foundry, Amazon Bedrock, Google Vertex AI and unmanaged providers, correlates the sensitive data connected to them, and maps findings to frameworks including NIST AI RMF 600-1 and the EU AI Act.

Runs AI models over the security data flowing through the Zero Trust Exchange, using attack graphs, user risk scoring and threat intelligence to score the probability of a breach, forecast an attacker's next tactics, and recommend policy changes before the attack completes.

AI-driven detection and diagnosis of digital-experience issues across the network and application delivery path.

Real-time guardrails for LLM traffic that inspect prompts and responses inline to block prompt injection, jailbreaks, sensitive-data leakage and toxic or off-topic output, deployed either as a proxy or as an API-based Detection as a Service.

Inline, AI-powered sandbox that analyzes unknown files in real time to block zero-day and file-based malware before it reaches endpoints, using AI/ML models trained on 600M+ samples.

Automated adversarial testing for AI applications and agents that runs simulated attacks using 25+ predefined and custom probes across text, image, voice and document inputs, scoring results for security, safety, hallucination and business alignment against MITRE ATLAS, NIST AI RMF and the OWASP LLM Top 10.