As organizations adopt AI, the attack surface expands. Security must move from model protection to prompt engineering defense and output validation.
Explore Our FrameworkTraditional security approaches fall short when protecting generative AI systems. We need a new paradigm.
As organizations rapidly adopt Large Language Models (LLMs) and generative AI, they're inadvertently creating new attack vectors that traditional security controls cannot adequately address. The unique characteristics of these systems - their probabilistic nature, training data dependencies, and prompt-based interfaces - require specialized security approaches.
Security must evolve from simple model protection to comprehensive defenses spanning prompt engineering, output validation, and continuous monitoring. This requires a systematic approach aligned with frameworks like NIST AI RMF and OWASP Top 10 for LLMs.
Addressing the most critical security risks for Large Language Model applications
Malicious inputs that manipulate LLM behavior, bypassing filters or executing unauthorized actions.
Use delimiters and parametrized queries to separate instructions from data. Implement input validation and sanitization.
Vulnerabilities arising from trusting LLM outputs without proper validation.
Treat LLM output as untrusted user input; encode before rendering. Implement output validation and sanitization.
Manipulation of training data to introduce vulnerabilities, backdoors, or biases.
Validate supply chain of data; use Software Bill of Materials (SBOMs) for datasets. Implement data provenance tracking.
Attacks that consume excessive resources, causing service degradation or increased costs.
Cap context window and resource usage per user. Implement rate limiting and resource monitoring.
Risks from compromised components, packages, or pre-trained models.
Vet third-party models and libraries. Maintain an AI component inventory and monitor for vulnerabilities.
LLMs may inadvertently reveal confidential data in responses.
Sanitize PII from training datasets. Implement data loss prevention and content filtering.
A practical guide to securing your LLM and generative AI implementations
Define clear guidelines for appropriate AI usage, data handling, and security requirements. Ensure all stakeholders understand their responsibilities.
Maintain human oversight for critical decisions, especially in regulated industries or high-impact scenarios.
Regularly test your AI systems against adversarial attacks, prompt injections, and jailbreak attempts to identify and mitigate vulnerabilities.
Implement continuous monitoring to detect performance degradation, concept drift, and hallucination patterns in model outputs.
Implement robust data anonymization and pseudonymization techniques to protect personally identifiable information in training data.
Structure your AI security program around the four core functions: Govern, Map, Measure, and Manage.
A systematic approach to managing AI risks throughout the lifecycle
Establish policies, procedures, and accountability structures for AI security. Define roles, responsibilities, and risk tolerance.
Identify and document AI systems, data flows, and potential vulnerabilities. Create an inventory of AI assets and their risk profiles.
Implement metrics and monitoring to assess AI system performance, security posture, and compliance with policies.
Continuously address identified risks, implement controls, and respond to incidents. Adapt to evolving threats and requirements.