/ AI SECURITY SERVICES
AI security services for enterprise AI, LLM, and generative systems: technical risk identification, attack surface analysis, and control design.
The risk profile of AI systems differs from traditional IT applications. Model behavior, input context manipulability, external tool connections, the quality of training and knowledge sources, and autonomous execution logics create entirely new vulnerabilities and operational risks.
Regulatory and governance requirements are necessary but do not prove the actual resilience of an AI system. True risks emerge at the level of models, prompt chains, data flows, permission structures, and integration points.
[ 01 ] / THREE DIMENSIONS
Our service portfolio is not organized around isolated controls or generic audit findings, but along three defining dimensions of AI security. This allows the organization to clearly see the type of risk it handles and where deep technical intervention is justified.
Focuses on shielding AI systems, models, and integration chains. Includes assessments and control design to evaluate vulnerabilities, operational risks, and exploitability of AI components.
Examines how AI enhances attacker capabilities and creates new threat patterns. The focus is on interpreting AI-backed attack models, pentest scenarios, and adversarial use cases.
Aims at utilizing AI on the defensive side to ensure capabilities remain reliable, verifiable, and risk-proportionate. The emphasis is on controlled and secure implementation.
[ 02 ] / SERVICE AREAS
Modular services adaptable to pilot systems, live AI applications, and complex, multi-component architectures.
Discovering architectural and operational risks in chatbots, copilots, knowledge assistants, and generative workflows.
Technical analysis of input manipulation, instruction override, and response generation bias with a validation mindset.
Security review of document sources, access logic, indexing chains, and response paths in retrieval-based systems.
Investigating attack surfaces of AI agents linked to tools, external APIs, and operational automation.
Evaluating how AI reshapes attack patterns and where it increases the efficiency of adversary capabilities.
Establishing AI for defensive use within detection, analysis, incident response, and automation environments.
[ 03 ] / METHODOLOGY
An AI security assessment is credible only if it moves beyond generic governance statements to focus on specific architectures, attack models, and operational chains.
Assessments start from the interconnected web of models, middleware, vector search, data sources, and API mechanisms.
The goal isn't just listing missing controls, but evaluating where and how the system can be exploited in real-world environments.
Technical findings are translated into organizational, operational, and reputational impacts to support better executive decision-making.
Outputs are prioritized, implementable frameworks rather than theoretical recommendation lists.
[ 04 ] / POSITIONING
Many AI offerings focus purely on policy and governance. While essential, these alone won't reveal where an enterprise AI can be manipulated or how data paths might be compromised.
Our approach assumes that AI security can only be managed proportionately if the organization understands the internal logic of the AI and the limits of its defensive deployment.
[ 05 ] / RELEVANCE
For systems accessing sensitive business, legal, financial, or technical information.
Where model responses directly affect reputation, contractual exposure, or customer experience.
When the organization needs to evaluate how AI changes attack patterns and adversary capabilities.
When AI is introduced to support detection, incident response, or security automation.
[ 06 ] / WHY QYNTAR
Built on cybersecurity, offensive, and architectural expertise, ensuring assessments stay technical and actionable.
We integrate system protection, adversarial assessment, and defensive AI deployment into one cohesive strategy.
Focus your resources where the actual business, operational, or regulatory impact is highest.
Findings turn into technical control plans and measures ready for real-world implementation.
AI security audits, LLM Red Teaming, and AI risk management.
AI security assessments, attack simulations, technical control validation, and consulting.
Contact is especially recommended if your organization is developing or integrating AI solutions and requires technical risk identification or regulatory compliance.
