AI Security

Using AI securely. Identify and address risks, data and model concerns, and potential attack or failure modes early and effectively.

AI Security focuses on security-relevant topics related to AI-based components and applications. The emphasis is on assessing project-specific risks, handling data and models, and anticipating potential attack scenarios and failure or misuse cases.

How we help

Typical situations and challenges.

Model behavior and misuse

"How do I protect my AI from malicious inputs and undesired behavior?"

Privacy and data quality

"How do I balance privacy, a­non­y­mi­za­tion, and high data quality?"

Reliability and explainability

"How do I achieve dependable results even where traditional ML models cannot provide guarantees?"

Operations and integration

"How do I operate AI securely and ensure it is used correctly?"

What we do

Tangible services and outcomes.

Analyze security-relevant properties of AI use cases - from data foundations and model behavior to the role of AI within the sys­tem.

Identify attack vectors, misuse scenarios, and sys­tem­at­ic model mis­be­hav­ior, and mitigate them in a targeted way.

Develop, integrate, and operate AI components under real-world security requirements - including self-developed trusted execution environments.

Build threat models for AI systems and translate them into verifiable requirements for data flows, interfaces, and controls.

Select appropriate mod­el­ing approaches from statistics, ML, and spe­cial­ized methods - fo­cus­ing on dependable results despite uncertainty.

Operate AI systems under strict SLA requirements; define tests for mis­be­hav­ior, edge cases, and attacks, and translate them into concrete meas­ures.

Questions?

Talk to us about your requirements, specific initiatives, or an active incident.

Get in touch