AI Security

We simulate scenarios that reflect real-world conditions, including unexpected input patterns, high-volume traffic spikes, and adversarial perturbations. Through these simulations we expose weaknesses that traditional testing rarely identifies, such as reliability gaps, hallucination spikes, performance bottlenecks, fail-open states, and systemic vulnerabilities. These insights help strengthen operational resilience and inform model hardening strategies.
 

AI systems today span multiple vendors, hosting models, orchestration tools and agentic architectures.

Our service provides coherent, comparable evaluation across all major environments, including:

• OpenAI, Anthropic, Google, Meta, and Mistral
• On-premise and VPC-based open-source models
• Agentic and orchestration frameworks such as CrewAI, LangChain and Azure AI Studio
• Traditional ML risk models, scoring systems, and NLP pipelines

This model-agnostic approach ensures consistent benchmarks and remediation guidance regardless of vendor, hosting architecture, or system complexity.
 

We deliver reporting tailored to financial-services control functions, including CRO, CISO, Model Risk, and Internal Audit. Outputs include:

• vulnerability matrices with severity scoring
• detailed attack evidence, exploit paths, and reproducible test cases
• remediation playbooks and actionable hardening recommendations
• documentation aligned with EU AI Act expectations, ISO 42001, and NIST AI RMF
• executive-level summaries linked to enterprise risk narratives

These deliverables support regulatory engagement, internal approvals, and audit requirements, ensuring that AI systems remain safe, robust, and compliant throughout their lifecycle.