AI Security

Before you can secure AI systems, you need to understand their specific attack surface. We analyze your AI architecture systematically — from data ingestion and model training through to inference in production. In doing so, we identify vulnerabilities such as insecure API endpoints, unprotected model artifacts, and missing input validation. The result is a prioritized risk matrix with concrete measures, aligned to your business risk and regulatory requirements such as the EU AI Act.

• Systematic identification and assessment of all AI-specific attack surfaces along the entire ML lifecycle — from data acquisition through training to productive deployment.
• Structured threat modeling according to established frameworks (STRIDE, MITRE ATLAS) adapted to AI architectures, including assessment of probability of occurrence and damage potential.
• Identification of vulnerabilities in data pipelines, model architectures and inference infrastructures as well as derivation of prioritized measures for risk minimization.
• Creation of an individual AI risk register that serves as the basis for your AI security framework and regulatory documentation requirements.
• Involvement of stakeholders from IT security, data science and compliance for a comprehensive risk assessment that equally considers technical and organizational dimensions.

Large language models are particularly susceptible to a new class of attacks: prompt injection, jailbreaking, indirect prompt injection via embedded documents, and data exfiltration through manipulated outputs. We implement multi-layered protection concepts — from input sanitization and output filtering, through guardrails and system prompt hardening, to real-time monitoring of suspicious interaction patterns. Our experience from operating our own LLM-based agent systems flows directly into the security of your systems.

• Analysis and hardening of LLM deployments against direct and indirect prompt injection attacks, including assessment of system prompt leakage and jailbreaking risks.
• Development and implementation of multi-layered input and output validation concepts that detect and neutralize malicious inputs before they influence the model or downstream systems.
• Security architecture review for LLM-based applications, including assessment of plugin ecosystems, tool-use interfaces and retrieval-augmented generation setups for attack potential.
• Design and implementation of guardrail systems and content filtering mechanisms tailored to your specific use cases and compliance requirements.
• Training and awareness for development teams on secure LLM integration, including secure coding guidelines and best practices for productive deployment of generative AI.

Classical penetration tests do not cover AI-specific attack vectors. Our AI penetration testing focuses specifically on machine learning systems: we test for adversarial examples, model inversion attacks, membership inference, and data poisoning. We use established frameworks such as OWASP ML Top 10 and MITRE ATLAS. You receive a detailed report with reproducible findings, risk assessment according to CVSS, and practical remediation recommendations.

• Conducting specialized AI penetration tests that specifically address AI-specific attack vectors — including model extraction, membership inference, adversarial input crafting and data poisoning simulations.
• Red team exercises for LLM-based systems and autonomous AI agents, where our experts simulate real attacker scenarios and uncover vulnerabilities in real time.
• Assessment of ML model solidness against deliberately manipulated inputs (adversarial examples) as well as analysis of model boundaries and misclassification potential.
• Detailed pentest reports with CVSS ratings for AI-specific vulnerabilities, clear action recommendations and tracking of measure implementation in the remediation process.

An AI security framework establishes the organizational guardrails for the secure use of AI. Together with you, we develop policies, processes, and controls that can be integrated into your existing ISMS. From model inventory and access controls and data classification through to incident response planning for AI-specific incidents. In doing so, we take into account regulatory requirements from the EU AI Act, DORA, and industry-specific standards.

• Development of a tailored AI security framework that brings together technical security requirements, organizational responsibilities and regulatory requirements (EU AI Act, DORA, ISO 42001) in a coherent set of rules.
• Creation and implementation of AI security policies, guidelines and processes for the entire AI lifecycle — from procurement and development to operation and decommissioning.
• Establishment of governance structures including definition of roles, responsibilities and escalation paths for AI security incidents as well as integration into existing ISMS structures.
• Support in the classification of AI systems according to risk classes in accordance with the EU AI Act and derivation of the resulting conformity requirements and documentation obligations.
• Training programs and awareness measures for executives, developers and users to embed AI security as a lived corporate culture.

Adversarial attacks aim to deceive ML models through deliberately manipulated inputs — often with changes imperceptible to humans. We harden your models through adversarial training, solidness testing, and the implementation of detection mechanisms. For computer vision, NLP, and tabular models, we apply specialized techniques that measurably increase the resilience of your system without significantly impairing model performance.

• Analysis of your ML models' susceptibility to adversarial examples and development of specific countermeasures such as adversarial training, input preprocessing and ensemble methods.
• Implementation of solidness tests and certification procedures that make the resilience of your models against known and novel adversarial attack classes quantifiable.
• Protection of training data pipelines against data poisoning attacks through implementation of data validation, anomaly detection and cryptographic integrity assurance.
• Consulting on the selection and configuration of solid model architectures as well as integration of defensive ML techniques into your existing ML development process.

AI systems require continuous monitoring — not only for technical availability, but for security-relevant anomalies. We implement monitoring solutions that detect suspicious patterns in model inputs and outputs: unusual query volumes, systematic probing attempts, or gradual drift through data poisoning. Integration into existing SIEM systems and defined escalation processes ensure that your security team can act immediately in the event of AI incidents.

• Establishment of AI-specific monitoring infrastructures that detect not only technical availability but also model drift, anomalous inference behavior and potential attack patterns in real time.
• Integration of AI security events into existing SIEM systems and SOC processes, including development of customized detection rules and alerting logic for AI-specific threat scenarios.
• Development and testing of AI incident response playbooks that define clear action instructions for various AI security incidents — from prompt injection attacks to compromised models.
• Conducting regular tabletop exercises and simulations of AI security incidents to strengthen your teams' response capabilities and identify weaknesses in your processes early.
• Forensic analysis after AI security incidents for root cause analysis, damage assessment and derivation of sustainable improvement measures — including regulatory documentation for reporting obligations.