AI Security: Protecting LLM, RAG and Agentic Systems
AI security protects models, data, prompts, retrieval, memory, tools, identities, APIs, orchestration, and infrastructure against unauthorized access, manipulation, disclosure, disruption, and destruction.
What is AI security?
An AI application remains a software system. Authentication, authorization, secure development, dependency management, logging, and incident response do not disappear when a model is added. They become more important because model behavior is probabilistic and because untrusted content may influence decisions across prompts, retrieval sources, memory, and tools.
A system prompt, jailbreak list, or one detection model is not a security boundary. Effective protection combines architecture-specific threat modeling, restricted authority, deterministic checks, reviewable evidence, monitoring, and repeatable evaluation.
AI Security and AI Safety are related, but not identical
| AI Security | AI Safety | |
|---|---|---|
| Primary concern | Protecting AI assets and behavior against unauthorized or adversarial actions | Reducing the risk that an AI system causes harm, whether or not an attacker is involved |
| Typical causes | Prompt injection, poisoned data, compromised dependencies, stolen credentials, malicious tool output | Incorrect predictions, unsafe decisions, inadequate oversight, unsuitable deployment, harmful behavior |
| Example | A retrieved document instructs an agent to disclose confidential data | A model gives unsafe guidance because its behavior was not sufficiently evaluated |
| Typical controls | Threat modeling, access control, input segregation, output validation, least privilege, monitoring, security testing | Use-case limits, human oversight, validation, transparency, fallback procedures, risk acceptance |
| Overlap | A security compromise can make a system unsafe. Both disciplines require lifecycle risk management, testing, and accountable ownership. | |
Security is one part of trustworthy AI, not a substitute for safety, privacy, reliability, or governance. Teams still need to identify which risk they are managing and who owns the corresponding control.
The AI attack surface
Data, models, and supply chain
Training data, fine-tuning sets, evaluation corpora, model artifacts, libraries, and third-party services can be poisoned, replaced, or misconfigured. Provenance, integrity checks, restricted publishing pipelines, and dependency controls matter before a model reaches production.
Prompts, instructions, and context
Prompt injection occurs when untrusted content influences model instructions or decisions. It can arrive directly through a user message or indirectly through documents, websites, email, tool responses, and retrieved knowledge. Trusted instructions and untrusted data must remain distinct in both architecture and policy.
Retrieval, embeddings, and memory
RAG adds ingestion pipelines, vector stores, retrieval rules, cache behavior, and tenant boundaries. Weak isolation can expose another user's content. Poisoned documents can manipulate answers, while persistent memory can carry malicious context into later actions.
Tools and AI agents
An agent may search, write files, call APIs, send messages, or modify records. This turns model output into operational impact. Tool authorization, narrowly scoped credentials, argument validation, approval gates, and deterministic policy checks are central controls. The model must not decide its own authority.
Application and infrastructure
AI systems remain exposed to conventional flaws such as injection, broken access control, insecure secrets, SSRF, vulnerable dependencies, and unsafe output handling. OWASP and MITRE frameworks support threat discovery, but neither replaces architecture-specific analysis.
Why Turkish and multilingual AI security matter
A control tested only in English has not been validated for a Turkish or multilingual deployment. Language changes phrasing, ambiguity, morphology, token boundaries, code-switching, and the ways instructions can be concealed inside ordinary content.
Production systems may combine a Turkish user request, an English system prompt, multilingual retrieval sources, and tools with structured syntax. Evaluation must represent that real operating context. It should also include legitimate requests that resemble attacks, because a defense with high attack recall but unacceptable false positives is not operationally secure.
AltaySec publishes a versioned Turkish conversation prompt-injection dataset as one reproducible input for this work. A public dataset is not a universal benchmark or a guarantee of production security; teams must adapt it to their architecture, threat model, and acceptance criteria.
How AltaySec approaches AI security
AltaySec is led by Fevzi Ege Yurtsevenler and Enes Deniz, two of the specialists who helped establish AI security as a technical field in Türkiye and continue to advance it. Deniz works across LLM application security, prompt-injection and jailbreak defense, AI agent and RAG security, AI red and blue teaming, security evaluation, and defensive engineering.
- Inventory the system. Map models, data sources, prompts, retrieval layers, identities, tools, dependencies, trust boundaries, and human decision points.
- Model applicable threats. Select threats based on architecture, access, deployment context, and potential impact.
- Test meaningful boundaries. Exercise direct and indirect prompt injection, sensitive-data paths, tenant isolation, tool authorization, output handling, and failure behavior. Include legitimate requests that resemble attacks.
- Capture reviewable evidence. Record inputs, configuration, model and tool behavior, timestamps, errors, expected outcomes, and explicit limitations.
- Reduce blast radius. Apply least privilege, data minimization, context segregation, deterministic validation, approval gates, and safe failure modes around probabilistic components.
- Turn findings into regression tests. Retest the same failure after remediation and during future model, prompt, RAG, tool, and policy changes.
This method is reflected in public work including the AI engineering primer for security professionals in the OWASP AI Exchange and AltaySec's open-source evidence center, where technical claims link to pull requests, merge commits, reports, and scoped validation results.
Explore the AI security topic cluster
Frequently asked questions
What is the difference between AI security and cybersecurity?
Cybersecurity protects systems, networks, software, and information. AI security applies those foundations while adding threats to AI-specific assets and behavior, including prompts, models, embeddings, training data, retrieval context, and agent tools.
Can a prompt filter prevent prompt injection?
No single prompt filter is a complete security boundary. Filters can support detection, but robust systems also limit privileges, separate trusted and untrusted context, validate tool calls and outputs, monitor behavior, and require human approval for consequential actions.
Is AI red teaming the same as a penetration test?
They overlap, but their scope can differ. AI red teaming examines model behavior, prompt and context manipulation, data exposure, tool use, and abuse paths. A complete assessment also covers the conventional application, API, cloud, identity, and supply-chain controls around the model.
How should an organization start securing an AI system?
Start with an inventory and an architecture-specific threat model. Identify the data, model, retrieval, tool, identity, dependency, and human-approval boundaries before selecting tests or buying controls.
Does passing an AI security test prove that a system is secure?
No. Test results apply to the evaluated version, configuration, model, dataset, and threat scope. Models, prompts, knowledge sources, tools, and dependencies change, so security requires repeatable evaluation, monitoring, and regression testing.
Sources and evidence boundary
This guide is educational and does not certify any system as secure. Findings and controls must be evaluated against the actual architecture, deployment, data, permissions, and threat model.