AI security researcher assessing a machine learning pipeline for adversarial risks
Security engineer reviewing data poisoning risks in an AI training dataset for a client
Security consultant assessing prompt injection vulnerabilities in a client LLM deployment
AI security team presenting model security findings to a CTO and engineering team
Security architect designing an AI governance and model security framework for a client
AI SYSTEM SECURITY
Artificial intelligence (AI) and machine learning (ML) systems introduce new attack surfaces — model inversion, data poisoning, adversarial inputs, and prompt injection that conventional tools do not detect. CyberHeals assesses AI/ML systems for these emerging vulnerabilities, reviews model pipelines, and advises on AI governance frameworks.
FULL AI SECURITY SCOPE
Assess, Review, Govern, and Monitor
AI/ML model security assessment and adversarial testing
Training data integrity and supply chain security review
AI governance framework development and regulatory alignment
CyberHeals AI security assessments identify prompt injection, adversarial input, and data poisoning risks that standard AppSec tools do not test for.
10+
CyberHeals delivers AI/ML security assessments across 10+ countries for organizations building or deploying AI-powered systems.
100+
Countries active globally
WHY TEAMS CHOOSE US
Built for AI and ML Security
Model Assessment
Adversarial testing and vulnerability assessment for ML models and AI-powered applications.
LLM Security
Prompt injection, jailbreak, and output manipulation testing for LLM deployments.
Data Pipeline Security
Training data integrity review covering poisoning, bias injection, and supply chain exposure.
AI Governance
AI risk governance frameworks aligned to EU AI Act, ISO 42001, and regulatory guidance.
Compliance Aligned
AI security assessments support EU AI Act, NIST AI RMF, and AI regulatory requirements.
Certified Experts
AI security researchers hold CISSP or ML security credentials with AI assessment experience.
01 / AI RISK
Security for AI, Not Around It
AI systems require AI-specific security testing — conventional tools miss prompt injection, model inversion, and data poisoning risks in the full AI stack.
LASTING SECURITY
From AI Deployment Risk to Secure Systems
10+
CyberHeals delivers AI/ML security programs across 10+ countries for organizations deploying AI systems.
CyberHeals AI security assessments cover prompt injection and jailbreak vulnerabilities in LLM deployments, adversarial input attacks causing model misclassification, training data poisoning and integrity risks, model inversion and membership inference attacks, and AI supply chain risks from third-party models and datasets. Both custom and third-party AI systems are in scope.
LLM security testing covers the OWASP Top 10 for Large Language Models — prompt injection, insecure output handling, training data poisoning, model denial of service, and supply chain vulnerabilities. We test system prompts, API endpoints, plugin integrations, and RAG pipelines for injection and data leakage risks in your specific deployment.
Data poisoning is the deliberate introduction of malicious samples into a training dataset to manipulate model behavior. CyberHeals assesses your training data pipelines for integrity controls, access governance, and supply chain risks from third-party datasets. For deployed models, we evaluate whether behavioral testing can detect poisoning effects.
The EU AI Act imposes risk-based obligations on AI systems — high-risk systems require conformity assessments, transparency documentation, and ongoing monitoring. CyberHeals maps your AI systems to the Act's risk categories, identifies compliance obligations, and develops governance frameworks, technical documentation, and monitoring controls your system requires.
Yes. We assess AI security for cloud-hosted services including Azure OpenAI, AWS Bedrock, and Google Vertex AI alongside on-premises and custom deployments. Cloud AI assessments cover API access controls, prompt injection risks, output filtering effectiveness, data residency compliance, and the security of fine-tuning and retrieval pipelines.
AI security assessments are scoped based on the type of AI system, the number of models or endpoints in scope, and the depth of testing required. We provide a fixed-price proposal after a discovery call covering your AI stack, deployment architecture, and compliance requirements. Ongoing AI governance advisory retainers are available for organizations in regulated sectors.
CyberHeals — global cybersecurity in 10+ countries
AI security researcher assessing a machine learning pipeline for adversarial risks
Security engineer reviewing data poisoning risks in an AI training dataset for a client
Security consultant assessing prompt injection vulnerabilities in a client LLM deployment
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AI security team presenting model security findings to a CTO and engineering team
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Security architect designing an AI governance and model security framework for a client
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