Overview
US companies are adding AI assistants, RAG search and autonomous agents to products and internal tools faster than security reviews can keep up. The NIST AI Risk Management Framework encourages testing AI systems before and after deployment. We test chatbots, retrieval pipelines and agents for prompt injection, data leakage, unsafe tool use and cross-tenant exposure.
Generative AI features change your threat model. An attacker no longer needs a code bug if they can persuade your model to ignore its instructions, reveal another user's documents, or call a tool on their behalf.
Our AI security assessments focus on your application — prompts, retrieval, tools, permissions and output handling — rather than the underlying foundation model. We test direct and indirect prompt injection, data leakage across users and tenants, excessive agency in agents, and unsafe handling of model output in downstream systems.
Beyond testing, we help you set AI usage policies, assess third-party AI tools and align with emerging frameworks such as the NIST AI RMF, ISO/IEC 42001 and the EU AI Act. Because most AI features sit on ordinary APIs, we usually combine AI-specific testing with a web and API assessment.
How testing works for US organizations
- Signed scope and written authorization before any testing, including hosting and cloud provider permissions
- Testing windows agreed around your US time zone at scoping, including after-hours windows for production
- Fully remote delivery, with internal and wireless access via VPN or a shipped test device, so no travel costs
- Reports mapped to SOC 2, PCI DSS, HIPAA and customer questionnaires, with a fixed price in USD
What's included
Our approach
- Architecture reviewModel provider, orchestration, retrieval, tools and data flows.
- Threat modelingMapping abuse cases to the OWASP LLM Top 10 and MITRE ATLAS.
- Adversarial testingManual and assisted prompt attacks across direct and indirect channels.
- Integration testingWeb/API testing of the surrounding application and tool endpoints.
- Report & guardrail designPrioritized fixes and secure-design recommendations.
What you receive
- Findings mapped to OWASP LLM Top 10 (2025)
- Prompt injection test corpus results
- Data isolation verification
- Agent permission review
- Secure AI design recommendations
Standards & frameworks
- OWASP Top 10 for LLM Applications 2025
- MITRE ATLAS
- NIST AI RMF
- ISO/IEC 42001
- EU AI Act
Frequently asked questions
Can prompt injection be fully prevented?
Not reliably with current models. The practical goal is to limit impact: treat model input as untrusted, restrict tool permissions and enforce authorization outside the model.
Do you test the AI model provider?
No. We test your application's use of the model. Model providers operate their own security programs under their terms.
Is this a separate engagement from a web app test?
It can be, but we usually combine them because most AI features are delivered through ordinary web and API endpoints.
Can you support US organizations from India?
Yes. All services are delivered remotely by our team in India. India is 9.5 hours ahead of US Eastern time during US daylight saving time (10.5 hours in winter) and 12.5 to 13.5 hours ahead of Pacific time. Meetings and testing windows are agreed around your US time zone at scoping, and 24/7 services run around the clock.
Where is our data processed?
Engagement data is handled by our team in India under NDA, with encrypted storage, least-privilege access and secure deletion at the end of the agreed retention period. Data-handling terms are agreed in the contract. If you need something specific, such as US-only data residency or a business associate agreement, raise it at scoping and we'll tell you plainly whether we can meet it.