Structured Information Platform

AI Security

AI security focuses on the security risks of artificial intelligence systems, LLM-based applications, RAG solutions, enterprise chatbots and agentic AI systems.

The goal of the AI Security Hub is to provide a structured English-language knowledge base on AI system vulnerabilities, attack techniques, data security issues, governance responsibilities and mitigation strategies.

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SECURITY NODE

STRUCTURE READY

[ 01 ] / CORE CONCEPT

What is AI security?

AI security focuses on protecting artificial intelligence systems, managing their risks and ensuring reliable operation. Its goal is to make AI models, LLM-based applications, RAG systems, agentic AI solutions and related data flows resilient against manipulation, data leakage, unauthorized access, faulty decisions and adversarial misuse.

Compared with traditional cybersecurity, AI security does not only examine infrastructure, application logic and access controls. It also addresses model behavior, training and knowledge sources, prompts, context handling, autonomous tool use and the controllability of AI-supported decision processes.

[ 02 ] / KEY DOMAINS

Key domains of AI security

AI security consists of several interconnected domains: technical protection, data security, model security, application security, governance and compliance controls.

LLM and prompt security

Prompt injection, jailbreaking, system prompt leakage, output manipulation and context manipulation are central security concerns in LLM-based systems.

Data security and RAG systems

AI systems often rely on internal documents, knowledge bases and business data. This makes authorization filtering, data leakage prevention, indexing logic and knowledge source protection critical security topics.

Models and attack techniques

AI models may be targeted by data poisoning, model stealing, model inversion, membership inference or adversarial attacks, which differ from traditional application security vulnerabilities.

AI governance and compliance

Secure AI adoption requires clear responsibilities, risk assessment, controls, logging, human oversight and regulatory compliance.

[ 03 ] / SERVICE

AI security audit and technical assessment

If an organization already uses or is preparing to introduce AI, LLM, RAG or agentic systems, the risks presented in this knowledge base can also be assessed in practice through an AI security audit, LLM security testing or AI red teaming.

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For general inquiries, professional discussions, or consultations related to AI security, you can reach out using the contact information below.

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