AI Security and Compliance Guide

Enterprise AI systems introduce novel attack surfaces that traditional cybersecurity frameworks do not adequately address. While standard security concerns like network security, access control, and data encryption apply, AI systems face additional threats targeting the model itself, its training data, and the unique interaction patterns of natural language interfaces. Understanding this expanded threat landscape is the first step toward building resilient AI deployments.

Model-level threats include adversarial inputs designed to cause misclassification or manipulation of outputs, model extraction attacks that reverse-engineer proprietary models through API queries, and model inversion attacks that extract training data from model outputs. For language models specifically, prompt injection is the most prevalent threat, where carefully crafted inputs manipulate the model into ignoring its instructions, revealing system prompts, or producing harmful outputs. These attacks can be indirect, embedded in documents that the model processes through RAG, making them particularly difficult to detect.

Data-level threats target the information pipeline that feeds AI systems. Training data poisoning introduces malicious examples that cause the model to learn incorrect or harmful behaviors. Data exfiltration through model memorization allows attackers to extract sensitive training data by prompting the model with partial matches. Supply chain attacks compromise pre-trained models, datasets, or software dependencies before they reach your infrastructure. The combination of these threats means that AI security requires a defense-in-depth approach with protections at every layer of the stack.

Protecting data throughout the AI lifecycle requires controls at ingestion, storage, processing, and output stages. Unlike traditional applications where data flows through well-defined paths, AI systems process data in complex ways that can cause unexpected information exposure. A comprehensive data protection architecture must account for the unique characteristics of AI data flows.

Training data protection starts with classification and access control. Inventory all data sources used for training, classify them by sensitivity level, and apply appropriate handling requirements. Implement data provenance tracking that records the origin, transformations, and approvals for every dataset used in training. For sensitive data, consider privacy-preserving techniques including differential privacy which adds calibrated noise to training data, federated learning which trains models across distributed datasets without centralizing the data, and synthetic data generation which creates training examples that preserve statistical properties without containing real records.

Inference-time data protection addresses the risk that user inputs and model outputs may contain sensitive information. Implement PII detection and redaction on both inputs and outputs, using pattern-based detection for structured PII like social security numbers and email addresses, and NER-based detection for names, addresses, and other contextual PII. Output filtering should scan for sensitive content that the model might generate from its training data, including code snippets, API keys, or personal information. Logging and audit trails must balance the need for observability with privacy requirements, potentially redacting sensitive fields in logs while maintaining enough context for debugging and compliance review.

Prompt injection is the most significant security challenge specific to LLM-based applications. It exploits the fundamental design of language models which process all text inputs uniformly, making it difficult to distinguish between legitimate instructions and malicious content embedded in user inputs or retrieved documents. Effective defense requires multiple layers of protection because no single technique is sufficient.

Direct prompt injection occurs when users craft inputs designed to override the system prompt or manipulate model behavior. Examples include instructions like "ignore all previous instructions and instead..." or more subtle approaches that gradually shift the model context. Defense against direct injection starts with robust system prompts that clearly delineate instructions from user content using formatting markers and explicit boundaries. Input validation filters known injection patterns before they reach the model. Output validation checks that responses comply with expected formats and content policies before returning them to users.

Indirect prompt injection is more insidious because the malicious content is embedded in documents or data that the model processes through RAG or tool use. An attacker who can insert a document into your knowledge base could include hidden instructions that are invisible to human readers but processed by the model. Defenses include separating retrieved content from instructions using structured prompt formats where the model is trained to treat retrieved content as untrusted, applying content sanitization to retrieved documents, implementing output monitoring that flags responses inconsistent with the system prompt, and limiting the model ability to perform sensitive actions based solely on information from retrieved documents. Regular red team testing should specifically target indirect injection vectors.

AI-specific regulation is rapidly evolving, with the EU AI Act serving as the most comprehensive framework currently in force. Organizations deploying enterprise AI must understand which regulations apply to their use cases and implement technical and organizational measures to achieve and maintain compliance. Proactive compliance reduces legal risk and builds trust with customers and regulators.

The EU AI Act classifies AI systems by risk level. Unacceptable risk systems, including social scoring and real-time biometric identification in public spaces, are prohibited. High-risk systems, including those used in employment, credit decisioning, education, and critical infrastructure, must meet requirements for risk management, data governance, technical documentation, transparency, human oversight, accuracy, robustness, and cybersecurity. Limited-risk systems must meet transparency obligations, including disclosing that users are interacting with AI. Most enterprise AI applications fall into the high-risk or limited-risk categories.

Beyond the EU AI Act, sector-specific regulations impose additional requirements. Financial services AI is subject to model risk management guidance from prudential regulators requiring model validation, ongoing monitoring, and independent review. Healthcare AI may fall under medical device regulations if it influences clinical decisions. Employment AI is subject to anti-discrimination laws and, in some jurisdictions like New York City, specific AI hiring law requirements for bias audits. Data protection regulations including GDPR and state privacy laws apply to all AI systems that process personal data. Your compliance program should map each AI application to applicable regulations, document the specific requirements, and implement controls that satisfy all applicable frameworks simultaneously.

Red teaming is the practice of systematically testing AI systems by adopting an adversarial perspective, attempting to find failure modes, security vulnerabilities, and harmful behaviors before they manifest in production. For enterprise AI, red teaming should be a regular practice integrated into the development and deployment lifecycle, not a one-time exercise.

A structured red teaming program defines scope, objectives, and methodology. Scope specifies which systems, models, and interaction patterns will be tested. Objectives may include finding prompt injection vulnerabilities, testing content safety filters, evaluating bias across demographic groups, assessing information leakage risks, and verifying compliance controls. The methodology should cover manual testing by experienced security researchers, automated testing using adversarial prompt libraries, and hybrid approaches that combine automated generation of test cases with human evaluation of results.

Red teaming categories for enterprise AI include security testing which targets prompt injection, data exfiltration, and access control bypass. Safety testing evaluates whether the system can be manipulated into producing harmful, toxic, or misleading content. Bias testing checks for discriminatory behavior across protected characteristics using paired examples that differ only in demographic attributes. Factuality testing probes the system tendency to hallucinate or present false information as fact. Robustness testing evaluates system behavior under unusual inputs including typos, multiple languages, code injection, and encoding tricks. Document findings with reproducible test cases, severity ratings, and recommended mitigations. Track the remediation of findings and retest to verify fixes.

AI systems can fail in ways that traditional incident response procedures do not anticipate. Model hallucination, adversarial exploitation, data leakage through model outputs, and discriminatory behavior all require specific response procedures. Organizations operating production AI must develop and rehearse AI-specific incident response plans that complement their existing security incident response capabilities.

AI incident classification should account for severity levels specific to AI failure modes. Critical incidents include confirmed data leakage through model outputs, successful prompt injection that bypassed safety controls, and discriminatory outputs that affected real decisions. High-severity incidents include sustained quality degradation affecting a significant user population, discovery of training data contamination, and compliance violations. Medium-severity incidents include isolated hallucination reports, minor bias detected in non-critical applications, and performance degradation below SLA thresholds. Each severity level should have defined response timelines, escalation paths, and communication templates.

Containment actions for AI incidents differ from traditional IT incidents. For a model producing harmful outputs, immediate containment may involve switching to a fallback model, enabling stricter output filtering, or temporarily disabling the affected functionality. For a data leakage incident, containment requires identifying what information was exposed, to whom, and whether the model needs to be retrained on a cleaned dataset. Post-incident review should analyze root cause, update threat models, improve monitoring to detect similar incidents earlier, and share learnings across the organization. Regular tabletop exercises should simulate AI-specific incidents to ensure the team can execute response procedures under pressure.

Technical controls alone are insufficient for securing enterprise AI. Security must be embedded in the culture and processes of every team that builds, deploys, or operates AI systems. This requires education, clear policies, and organizational structures that make security a shared responsibility rather than a bottleneck imposed by a separate team.

Security training for AI teams should cover the AI-specific threats discussed in this guide, with hands-on exercises that demonstrate real attacks against LLM-based systems. Data scientists and ML engineers need to understand how their design decisions affect security, such as the risk of including sensitive data in training sets, the importance of output validation, and the implications of model architecture choices for adversarial robustness. Application developers who integrate AI services need to understand prompt injection, proper input sanitization, and secure API integration patterns. Product managers need to understand the security implications of AI feature decisions so they can make informed trade-offs.

Establish an AI security champions program that embeds security expertise within each team building AI applications. Champions receive advanced training, participate in red team exercises, review AI designs for security considerations, and serve as the first point of contact for security questions. Create an AI security review process that evaluates new AI applications before production deployment, covering threat modeling, data classification, access control design, output validation, monitoring strategy, and incident response procedures. This review should be proportional to risk, with lightweight reviews for low-risk applications and comprehensive assessments for high-risk use cases. The goal is to make security a natural part of the AI development process rather than a gate that teams try to circumvent.