Most organizations discover AI vulnerabilities after an incident. TechAble Secure's Risk Assessment Tool surfaces them before deployment — providing a structured, repeatable process to evaluate the security posture of any AI system.
Deploying LLM applications and AI copilots in production environments.
Implementing AI agents with tool access and autonomous capabilities.
Organizations subject to AI-specific compliance requirements.
Preparing for enterprise customer or investor security due diligence.
Adopting AI systems requiring accountability and auditability.
Building AI-native products needing security validation before launch.
Every TechAble Secure assessment targets AI-native risk vectors that conventional security tools cannot detect.
Evaluates model exposure, fine-tuning risks, and behavioral anomalies across leading AI models and custom deployments.
Identifies direct, indirect, and multi-turn prompt injection vulnerabilities across the full input surface of AI applications.
Assesses tool-use permissions, memory architecture, and authorization boundaries in autonomous AI agent systems.
Reviews data ingestion, labeling, and model training workflows for supply chain threats and poisoning vulnerabilities.
Maps current AI governance posture against NIST AI RMF, EU AI Act, ISO/IEC 42001, and applicable sector regulations.
TechAble Secure follows a consistent five-step assessment process grounded in MITRE ATLAS and OWASP LLM Top 10 frameworks.
We map all AI systems, models, agents, pipelines, and data flows to define a complete assessment scope. No system is assessed blind — every component is documented before testing begins.
Using MITRE ATLAS and OWASP LLM Top 10, we build a threat model specific to the client's AI architecture — identifying the most relevant attack vectors before any testing begins.
Controlled adversarial tests across prompt injection, model manipulation, and agent exploitation vectors — executed with care to avoid production impact.
Assessment of AI policies, audit trails, human oversight mechanisms, and regulatory readiness against NIST AI RMF, EU AI Act, and sector-specific requirements.
Findings delivered with severity ratings, business impact context, and a prioritized remediation roadmap — with a board-ready executive briefing included.
Conventional penetration testing examines deterministic software. AI systems are probabilistic, context-sensitive, and capable of emergent behavior. The TechAble Secure methodology targets AI-native risk vectors that conventional tools cannot detect: prompt injection, model manipulation, agent authorization failures, and governance accountability gaps.
Tests deterministic software. Cannot detect prompt injection, emergent behavior, or AI governance gaps.
Purpose-built for probabilistic AI systems. Covers LLMs, agents, data pipelines, and governance accountability.
Miss AI-specific attack surfaces. Cannot model multi-turn adversarial scenarios or agent privilege escalation.