Secure Enterprise AI with Automated AI Red Teaming
Test, secure, and deploy enterprise AI with speed and confidence
Continuously test your AI systems for security and safety risks
Run high-scale vulnerability assessments and simulate domain-specific attacks on your AI systems from build to runtime.
FULL TESTING COVERAGE
With 25+ predefined and continuously updated testing probes, your organization can streamline the AI life cycle and ensure protection against emerging threats.
12x
99%
>75%
FULL TESTING COVERAGE
Leverage unmatched red teaming coverage with 25+ prebuilt probes for all relevant risk categories.
- Fine-tune each probe for on-domain testing
- Prioritize test criteria to match your preferences
- Fully automated, end-to-end AI security tests
CUSTOMIZABLE PROBES
Create your own, fully custom AI assessments to test for specific risk scenarios and security criteria.
- Define domain-specific tests for your use case
- Assess the effectiveness of active AI guardrails
CUSTOM DATASET UPLOADS
Gain full control of your AI red teaming by uploading predefined datasets tailored to your threat models.
- Run targeted evaluations with custom datasets
- Leverage fully on-domain testing capabilities
MULTI-MODAL TESTING
Simulate attack scenarios with different input types to ensure robust security of multi-modal AI assistants.
- Text
- Voice
- Images
- Documents
TRACK & REMEDIATE ISSUES
Improve your AI's security posture with dynamic remediation steps and track issues in external tools.
- Get tailored help based on discovered risks
- Keep issues tracked in Jira and ServiceNow
AUTOMATED POLICY MAPPING
Get automated compliance alignment checks based on discovered risks in your AI systems.
- MITRE ATLAS™
- NIST AI RMF
- OWASP® LLM Top 10
- Google SAIF
- EU AI Act
- ISO 42001
- DORA
- Databricks DASF
Integrations
Our team is constantly adding more connectors.
Connect your AI systems to the Zscaler platform in a few simple steps, no coding required.
Our advanced API integration allows for flexible connections to any type of endpoint.
Connect seamlessly to the most popular
Connect AI systems built on top of leading commercial and open source models.
Learn more about Zscaler's automated AI Red Teaming
Zscaler Advantage
Speed up AI adoption without compromising on security
Our platform accelerates AI deployments, reduces security overhead, and prevents high-impact incidents proactively and in real time.
Without Zscaler
With Zscaler
Without Zscaler
Security bottlenecks delay deployment
AI initiatives stall due to manual testing, fragmented ownership, and lack of scalable security workflows.
With Zscaler
Automated red teaming at scale
Run scalable, continuous testing to surface vulnerabilities earlier and reduce time-to-remediation across all AI workflows.
Without Zscaler
Limited visibility into AI risk surface
Security teams lack the tools to continuously map, monitor, or validate dynamic LLM behavior and vulnerabilities.
With Zscaler
Real-time AI risk surface visibility
Continuously monitor your entire LLM stack—including prompts, agents, and runtime behavior—from a single control point.
Without Zscaler
Inconsistent compliance and governance
Meeting evolving regulations requires constant manual tracking, increasing risk of audit failure or policy misalignment.
With Zscaler
Streamlined compliance and policy alignment
Track AI security standards with automated insights and audit-ready reporting that evolve with global regulations.
Without Zscaler
Isolated tracking of AI risks
No central view of AI security posture—red teaming, runtime analysis, and policy coverage live in separate tools (if at all).
With Zscaler
Unified platform for full life cycle AI security
Centralize AI security operations—from red teaming to runtime protection and governance—in one purpose-built platform.
FAQ
Automated AI red teaming uses advanced tools and simulations to test AI systems for vulnerabilities, security risks, and unintended behaviors. It enables continuous assessments by simulating attacks and stress-testing AI applications during their development and runtime. This ensures the AI remains robust, aligned with business objectives, and protected against emerging threats.
Automated AI red teaming detects critical risks in AI systems across all risk categories, aligned with AI security frameworks like MITRE ATLAS™, NIST AI RMF, and OWASP® LLM Top 10. This includes:
- Prompt injections that manipulate AI outputs
- Off-topic responses or hallucinations in AI outputs
- Social engineering attacks that exploit user interactions
- Vulnerabilities in multi-modal input methods, such as text, voice, images, or documents
- Domain-specific risks based on specific industries or use cases
Automated AI red teaming seamlessly integrates in CI/CD pipelines to continuously test AI applications for safety and reliability at every stage of their life cycle. It evaluates generative AI applications by:
- Simulating malicious prompts from diverse user personas to uncover vulnerabilities in interaction scenarios
- Stressing security guardrails with predefined and custom probes for safety and security
- Testing multi-modal inputs, such as text, images, voice, and documents, to simulate real-world attacks
- Benchmarking AI filters and evaluating existing safeguards to improve security without compromising quality
- Running domain-specific assessments that are tailored to the application's industry and purpose
Automated red teaming should be conducted continuously to ensure AI systems have ongoing protection against evolving threats. Regular risk assessments are vital to track vulnerabilities, adapt to emerging attack vectors, and quickly remediate issues. With red teaming capabilities integrated in the CI/CD pipeline, AI systems benefit from end-to-end security testing throughout development and runtime. Continuous testing enhances security while ensuring compliance with evolving AI security frameworks and regulations.