Zscaler (NASDAQ: ZS) announced the acquisition of SPLX, a young pioneer in AI security. With this move, it will strengthen its Zero Trust Exchange™ platform by adding capabilities such as shift-left discovery of AI assets, automated red teaming, and end-to-end governance. The company’s stated goal is clear: protect AI investments from development through production, especially as the industry is forecasted to spend over $250 billion on AI infrastructure before the end of 2025.
“AI creates enormous value, but it only materializes when it can be secured,” summarized Jay Chaudhry, Founder and CEO of Zscaler. “With SPLX’s technology and the AI intelligence of Zero Trust Exchange —which classifies, governs, and prevents leaks of sensitive data in prompts, models, and outputs— we will secure the entire AI lifecycle on a single platform.”
The acquisition occurs within a context of expanding attack surface, shadow AI, and agent adoption in real-world processes. Zscaler aims to prevent the deployment of models, workflows, and tools around RAG and MCP (Model Context Protocol) from becoming a new backdoor for data theft, output manipulation, or lateral movement between agents and services.
What SPLX Adds to Zscaler’s Platform
Zscaler will integrate SPLX’s technology and team as a native AI security layer within Zero Trust Exchange™, consisting of four functional blocks:
1) AI Asset Discovery and Risk Assessment (shift-left)
Not just public catalogs of generative AI or cloud providers: discovery extends to internal models, pipelines, code repositories, RAGs, MCP servers, and flows across both public and private environments. The aim is to maintain a live inventory, with calculated risks per asset, before they reach production.
2) Automated Red Teaming and Remediation
SPLX offers a motor with over 5,000 specific AI attack simulations — including prompt injections, jailbreaks, data exfiltration, malicious function calls, and tool abuse — that can be run from development to production. The system prioritizes findings and suggests real-time remediation, enabling teams to correct swiftly without waiting for manual audits.
3) Runtime Guardrails and Prompt Fortification
Zscaler enhances its AI Runtime Guardrails — which already prevent data leaks and attack chains between AI apps and LLMs — to deepen in development environments (IDE, CI/CD) and automate guardrail enforcement for risky assets (e.g., agents with access to sensitive data or tools with critical actions). Prompt fortification is integrated as a policy, not just a best effort.
4) Governance and Compliance
A unified framework moves from reactive defense to proactive protection: policies by model, environment, person, and data; audit logs and evidence; controls for emerging governance frameworks (e.g., data controls and model risk). The goal is for compliance, security, and product teams to speak the same language.
“Zscaler and SPLX share a vision: tackling the new attack surface created by AI infrastructure expansion,” stated Kristian Kamber, CEO and Co-founder of SPLX. “Together, we will bring our innovation to one of the most trusted security platforms in the world, ensuring AI innovation keeps pace with adoption.”
Why This Matters: AI Security Is No Longer Just “Prompt Filtering”
The pace of investment — over $250 billion by 2025, according to Goldman Sachs — has transitioned AI from standalone prototypes to production systems. These include LLMs orchestrating tools, agents with memory, RAGs connected to internal sources, and MCP for standardized app-model integrations. This shift introduces new and known risks:
- Discovery: AI is distributed — models are in multiple clouds, repositories, pipelines, and feature stores. Knowing what exists and where data lives is the first step.
- Unsafe Development: hardcoded prompts, exposed keys, repositories with sensitive datasets, testing with production tokens… A shift-left approach must include AI, not just code.
- Targeted Attacks: injecting prompt injections, directed hallucinations for action induction, abusing agents and functions, exfiltration through outputs or logs, and poisoning context data.
- Governance and Guardrails: defining who can use which model, with which data and tools, under which policies, and tracking auditable traces.
Zscaler’s approach is that all these elements — inventories, offensive testing, execution controls, and compliance — live together on the same platform, tightly linked to data protection and the Zero Trust security model already in place for applications, users, and workloads.
Who Benefits (and How)
- CISOs and GRC teams: a single dashboard of AI assets with risk assessments, policies, and audit trails. Less spreadsheets, more observable controls.
- MLOps / Data Platforms: automated red team testing integrated with CI/CD, providing contextual remediation. Discovery of RAGs and MCP (self-hosted) solutions.
- Product Engineering: guardrails at runtime that prevent leaks and abuse without slowing delivery; prompt hardening as a policy component, not just an informal guideline.
- Compliance: support for emerging governance frameworks with traceability by prompt, model, and output; governance of sensitive data at each point in the AI process.
How It Fits with Zero Trust Exchange
Zscaler leverages massive telemetry and native data protection (classification and loss prevention) across traffic, applications, and users. By integrating SPLX:
- The AI inventory (models, workflows, RAG, MCP, repos) is discovered and tagged within the same framework of identity + application + data.
- The red teaming engine is linked with CI/CD and environments (dev, test, prod), informing policy.
- Guardrails are applied in runtime — between AI apps and LLMs, or between agents — blocking exfiltration and attacks without disrupting user experience.
- Governance and compliance benefit from consistent logging and centralized enforcement.
Operationally, the promise is to reduce MTTD/MTTR of AI-related incidents 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