Security assessments, red teaming and governance for LLM applications, retrieval pipelines and AI agents, so you can approve AI deployments with evidence rather than assumptions.
Typical triggers: a customer-facing assistant launch, an agent being given access to internal systems, a board or regulator question about AI risk, or an enterprise customer's security questionnaire.
Most AI incidents come from the system around the model: content it trusts when it should not, tools it can call without enough restriction, and data it retrieves without checking who is asking.
Conventional penetration tests rarely exercise these paths. We test the whole chain, from the prompt and retrieved documents through the orchestrator to the APIs and records an agent can reach.
Each service can run on its own or be combined into one assessment. Availability is shown on every service.
A structured review and test of an LLM application: prompts, context handling, output handling, authentication and integration points.
OWASP LLM01, LLM05, LLM07, LLM10
Assessment of agents that plan, remember and act: what they can reach, what can steer them, and what stops unsafe actions.
OWASP LLM06 Excessive Agency; OWASP GenAI agentic AI guidance
Goal-driven adversarial exercises that emulate a motivated attacker against your AI system and its business rules.
MITRE ATLAS techniques; OWASP LLM Top 10
Testing of direct user injection and of instructions hidden in documents, emails, web pages and tool outputs.
OWASP LLM01 Prompt Injection
Checks whether models, prompts, logs or retrieval expose personal data, secrets or confidential records.
OWASP LLM02, LLM07; DPDP and GDPR context
Assurance that retrieval returns the right data to the right user, and nothing an attacker planted.
OWASP LLM04, LLM08
Review of third-party models, APIs, plugins, datasets and libraries your AI system depends on.
OWASP LLM03 Supply Chain; NIST AI RMF
Governance that lets you adopt AI with clear accountability, delivered with our Consulting practice.
ISO/IEC 42001; NIST AI RMF; EU AI Act where in scope
Design-stage threat modeling and independent assurance reports for leadership, customers or auditors.
MITRE ATLAS; NIST AI 600-1
Repeatable evaluation suites run on model, prompt or tool changes. Being developed for delivery through Managed Services.
Available today as periodic retesting; automated delivery in development
We agree scope per system. These are the components most assessments include.
We use published, community-maintained references so findings are traceable and comparable over time.
Agree systems, environments, data handling and stop conditions in writing before any testing.
Map assets, trust boundaries and abuse cases against OWASP and MITRE ATLAS.
Manual adversarial testing, supported by automated evaluation for breadth and repeatability.
Prioritize by impact, support remediation and verify the fixes.
Tools are selected per engagement and may include open-source projects such as PyRIT, garak and promptfoo, plus custom harnesses for your application. Tools support our testers; they do not replace manual analysis.
Framework and tool references describe our methodology and do not imply endorsement by, or partnership with, their maintainers.
Written for two audiences: engineers who must fix issues, and leaders who must decide on risk.
A fixed-scope assessment of one AI application, RAG pipeline or agent workflow, with a retest.
Objective-driven attack simulation across one or more AI systems and their business processes.
AI inventory, risk classification, policy and ISO/IEC 42001 readiness, linked to technical testing.
Share what you are building and when it goes live. We will propose a test scope that fits your timeline.
A guided menu, not an AI chatbot. It only uses published ZYKAV service information.