Security Testing for AI Systems
Find vulnerabilities across your AI applications, models, agents, data, and integrations before they become real security incidents.
AI Creates New Risks. We Help You Find Them.
Traditional application security is still essential. But AI introduces new connections, data flows, and behaviors that need to be tested too.
We help you answer the critical security questions:
- Can the AI be manipulated?
- Can sensitive data leak?
- Can an agent take actions it shouldn’t?
- Can connected systems be abused?
- Where could an attacker get in?
AI Security Testing, End-to-End
Choose the assessment you need or let our security team help define the right scope.
AI Application Pentesting
Test AI-powered applications for exploitable security gaps across the application, APIs, integrations, and underlying AI components.
Agentic AI Pentesting
Test AI agents, their permissions, tools, actions, and connected systems.
LLM Security Testing
Test how your LLM-based systems respond to malicious, unexpected, or manipulated inputs.
RAG Security Testing
Assess the systems connecting your models to enterprise knowledge and data.
MCP Security Testing
Test MCP servers, tools, connections, permissions, and the access they give AI systems.
AI Red Teaming
Challenge AI systems from an attacker’s perspective to uncover weaknesses that standard testing may miss.
AI Architecture & Threat Modelling
Review your AI architecture before security problems are built into it.
Continuous AI Security Testing
Re-test AI systems as models, agents, tools, integrations, and applications change.
From AI Security Questions to Action Plan
Our engagement is built around your environment and priorities.
- 01
Scope
Tell us what you’re building and what you need tested.
- 02
Assess
Our security team tests the relevant AI applications, systems, and connections.
- 03
Review
Findings are analysed and prioritized.
- 04
Report
Receive clear findings and actionable remediation guidance.
- 05
Re-Test
Validate fixes or continue testing as your AI environment evolves.
AI Security Expertise Without the Enterprise Price Tag
Every AI environment is different, so we price each engagement around your actual systems, testing requirements, and scope.
- Competitive pricing
- Clear scope
- No oversized security engagement
Need several services?
We can combine AI application, LLM, RAG, agent, MCP, architecture, and red-team testing into one engagement.
Why Zazmic for AI Security?
Security expertise meets hands-on AI engineering.
AI + Security in One Team
Test AI systems with people who understand how they are built and deployed.
More Than One Layer
Coverage across AI applications, models, agents, data, integrations, and cloud environments.
Built Around Real Risk
Focus testing and recommendations on issues your team can act on.
Ready to Scale
Start with a focused assessment or make testing part of your ongoing AI security program.
Don’t Wait for Production to Find the Weak Spot
Whether you’re launching a new AI application or already running agents in production, find the risks before they turn into incidents.
FAQ
Security for AI Systems assess security risks across AI-powered applications and the systems around them. Depending on the engagement, testing can cover AI applications, LLMs, RAG systems, AI agents, MCP integrations, architecture, and other connected components.
AI application pentesting evaluates an AI-powered application for security weaknesses that could be exploited by an attacker. The exact testing scope depends on the application and architecture.
LLM security testing focuses on security risks associated with applications built around large language models and how those models interact with users, data, applications, and connected systems.
RAG security testing assesses security risks around systems that retrieve information from enterprise data sources and use that information to generate AI responses.
Agentic AI pentesting assesses AI agents and the applications, tools, data, permissions, and workflows those agents can interact with.
MCP security testing evaluates security risks in environments using the Model Context Protocol to connect AI systems with tools, data sources, and external services.
AI red teaming uses adversarial testing to identify weaknesses in an AI system by deliberately challenging its security controls and behavior.
AI threat modeling identifies potential threats, attack paths, trust boundaries, and security weaknesses in an AI architecture before or alongside implementation.