How to Secure Your GenAI Systems: A Step-by-Step Guide

Your tech stack keeps growing and so do your concerns about the security of your GenAI systems.

Your enterprise is not alone, more than 85% of organizations are deploying AI in cloud environments and GenAI security has become a top priority. From our conversations with 170+ CISOs, one concern keeps surfacing: how to stay off the growing list of high-profile data breaches?

Companies are moving fast with AI adoption - 42% have already implemented LLMs in functions of all types and 40% are learning about AI implementation. The need for strong security measures has never been more pressing.

What do the stats say?

The investment in AI technology is substantial. Organizations spend an average of 3.32% of their revenue on AI initiatives. For a $1 billion company, this means about $33.2 million each year. Data privacy and security still pose major barriers to AI adoption. The OWASP Top 10 for LLMs and Generative AI emphasizes critical GenAI security risks that your organization needs to address, like prompt injection attacks and data leakage.

Self-hosted AI adoption has seen a dramatic increase from 49% to 74% year over year. Companies want complete data privacy and control. A detailed GenAI security framework with proper controls has become a strategic necessity, not just an operational concern.

Understand the Key GenAI Security Risks

Organizations need to understand the basic risks that threaten GenAI systems before securing them. Research shows that 27% of organizations have put a temporary ban on generative AI because of data security concerns. On top of that, about 20% of Chief Information Security Officers say their staff accidentally leaked data through GenAI tools. Let’s get into the biggest security risks your organization should know about:

Prompt injection and jailbreaks

Attackers can manipulate LLMs through prompt injection. They craft inputs that make the model ignore its original instructions and follow harmful commands instead. This happens because of how models process prompts, which can make them break guidelines or create harmful content. Tests on popular LLMs show this is a big deal as it means that attack success rates are over 50% across models of different sizes, sometimes reaching 88%.

Jailbreaking is a specific type of prompt injection. Attackers bypass the model’s original instructions and make it ignore established guidelines. These attacks could lead to unauthorized access, expose sensitive information, or run malicious commands.

Data leakage and privacy violations

Data leaks happen when unauthorized people get access to information. GenAI systems face several ways this can happen:

• Training data with sensitive details shows up in outputs

• Models copy training data word-for-word instead of creating new content

• Attackers use prompt injection to steal confidential information

• Model outputs travel through networks without encryption

Data could leak when the system uses one user’s input as learning material and shows it to other users. This becomes especially risky when AI systems index corporate data of all sizes in enterprise search.

Model poisoning and backdoors

Attackers can poison AI models by messing with their training data. They introduce weak spots, backdoors, or biases during pre-training, fine-tuning, or embedding.

These attacks come in two forms:

• Targeted attacks: The model misclassifies specific inputs

• Non-targeted attacks: The model’s overall performance gets worse

Backdoor attacks are particularly dangerous. Poisoned data creates hidden triggers that activate specific harmful behaviors when found, and they might stay hidden until someone uses them.

Adversarial inputs and output manipulation

Attackers craft special inputs to trick AI algorithms into making wrong predictions or classifications. These attacks take advantage of machine learning models’ weak spots.

Teams test ML models by feeding them harmful or malicious input. These inputs often have tiny changes that humans can’t see but affect the model’s output dramatically. A team at MIT showed this by tricking Google’s object recognition AI - it saw a turtle as a rifle after they made small pixel changes.

Over-permissioned AI agents

AI agents create more security risks as companies blend them with more internal tools. These systems often need broad access to multiple systems, which creates more ways to attack them.

AI assistants are changing from simple RAG systems to autonomous agents with unprecedented control over company resources. Unlike regular software that behaves predictably, AI agents make their own decisions that could interact with systems in unexpected ways and create security problems.

You can reduce these risks by using zero-trust methods. Give AI agents only the permissions they need for specific tasks. Add continuous authentication and run them in sandboxed environments.

Map Your GenAI System Lifecycle

GenAI security demands a detailed understanding of the system lifecycle. Research shows that data teams dedicate 69% of their time to data preparation tasks. This highlights how crucial this stage is in developing secure GenAI systems.

Data collection and preparation

High-quality data forms the foundation of any secure GenAI system. Your data collection process should include strict governance and categorization to work optimally. Start by separating sensitive and proprietary data into secure domains that prevent unauthorized access. A detailed data cleaning process should handle outliers, missing values, and inconsistencies that might create security vulnerabilities.

Data formats need standardization to maintain consistency. Automated validation processes should verify data quality by checking accuracy, completeness, and timeliness. Your data preparation must meet regulatory requirements like GDPR and HIPAA to stay compliant.

Model training and fine-tuning

Pre-trained models adapt to your specific security requirements through fine-tuning with targeted training datasets. Structure your training data as examples with prompt inputs and expected response outputs. The process works best with 100-500 examples based on your application.

Key hyperparameters need monitoring during training:

• Epochs (recommended default: 5)

• Batch size (recommended default: 4)

• Learning rate (recommended default: 0.001)

Security-sensitive applications might benefit from techniques like Reinforcement Learning from Human Feedback (RLHF). These techniques help arrange model behavior with your organization’s security values. They serve as good starting points and not hard limits.

Evaluation and testing

To prevent security issues before deployment, evaluate your GenAI model using measures that match your use case. For classification, consider accuracy, precision, and recall; for text generation, use BLEU, ROUGE, or expert review. Use explainability methods like SHAP and LIME together with quantitative fairness checks (for example, demographic parity) to identify bias. Challenge the model with adversarial inputs to confirm it resists malicious manipulation. Finally, test on entirely new or shifted data to verify safe and reliable behavior under unfamiliar conditions.

Deployment and monitoring

Continuous monitoring maintains security after deployment. Model drift tracking helps identify when retraining becomes necessary. Immediate monitoring of key security metrics should include response time, throughput, error rates, and resource utilization.

Data quality monitoring plays a vital role. Watch for anomalies, missing data, and distribution changes that could affect security. Automated retraining processes should kick in when performance drops. This ensures your GenAI system’s security throughout its operational lifecycle.

Implement Core GenAI Security Controls

The security controls become vital after mapping your GenAI system lifecycle. Organizations that make use of information from AI-powered security solutions see a 40% decrease in successful unauthorized access attempts. Here’s a guide to set up core security controls for your GenAI systems:

Use AI-specific data loss prevention (DLP)

Traditional DLP systems create too many false positives and overwhelm security teams. AI-powered DLP solutions provide better results through:

• Contextual understanding: AI-specific DLP grasps content context instead of just blocking keywords. Traditional systems block all emails with “confidential,” but AI-powered DLP knows when documents move securely within the company.

• Behavioral analysis: These systems look beyond content. They examine user behavior and connection patterns to spot potential data loss incidents accurately.

Apply role-based access and encryption

Role-based access control (RBAC) creates detailed protections for GenAI systems:

• Define roles and map permissions: Your GenAI system needs specific permissions for each role that interacts with it. Azure OpenAI offers roles like “Cognitive Services OpenAI User” and “Cognitive Services OpenAI Contributor” with different access levels.

• Apply layered RBAC: RBAC works at both end-user layer and AI layer. This controls who can access AI tools and what data the AI can access based on user permissions.

• Enhance with encryption: Confidential computing uses Trusted Execution Environments (TEEs) to separate data and computation. Homomorphic encryption lets you work with encrypted data without decryption for advanced needs.

Scan models for vulnerabilities

Regular checks protect against model exploitation:

• Implement scanning tools: Tools like Giskard help you spot common LLM vulnerabilities such as hallucination, prompt injection, and information disclosure.

• Monitor model behavior: The system needs regular checks for unusual patterns that show potential compromise or adversarial manipulation.

Use AI security posture management (AI-SPM)

AI-SPM gives you a reliable security overview of your GenAI ecosystem:

• Discover AI components: A complete list of AI services, models, and components prevents shadow AI in your environment.

• Assess configurations: The AI supply chain needs checks for misconfigurations that might cause data leaks or unauthorized access.

• Monitor interactions: Your system should track user interactions, prompts, and model outputs constantly to catch misuse or strange activity.

Test and Monitor with Red Teaming and Runtime Tools

Security protocols for GenAI systems need active testing and non-stop monitoring. Studies show that regular testing helps spot vulnerabilities before attackers can exploit them. Here’s a practical guide to test and monitor your GenAI systems:

Simulate prompt injection and adversarial attacks

Red teaming for GenAI tests the model by trying to make it generate outputs it shouldn’t. This active approach helps find security gaps that basic testing might miss. Here are key techniques to consider:

• Run automated red teaming to spot weaknesses early

• Test system defenses with realistic adversarial inputs

• Use AI-powered attack simulations to check model strength

• Try jailbreaking by asking models to act as rule-breaking characters

Many teams now rely on “red team LLMs” that create diverse attack prompts endlessly, which makes testing more complete.

Monitor inference traffic and API usage

Non-stop monitoring helps spot unusual patterns that might signal security issues. Key steps include:

• Set up immediate monitoring tools for all AI interactions

• Keep track of token usage through APIs like CountTokens to manage costs

• Apply anomaly detection algorithms to spot suspicious behavior

• Create alerts when usage hits specific limits

Use runtime agents to protect systems

Runtime security tools guard against various GenAI threats effectively:

• Add AI Runtime Security APIs to stop prompt injections and data manipulation

• Build stronger defenses by mixing semantic guardrails with threat detection

• Set up AI gateways to check interactions and catch threats across systems

• Add immediate oversight to spot delays and security risks

Log and audit AI decisions

Detailed audit logging captures key data about AI operations:

• Record each interaction with user details, prompts, and responses

• Watch model behavior to ensure outputs match expected patterns

• Keep secure audit trails that help with compliance

• Check logs to find compliance issues or policy breaks

Teams that use detailed audit logging cut their manual compliance work by 80% and catch threats faster through immediate detection.

Governance, Templates and Policy Frameworks

Technical safeguards need proper governance structures that are the foundations of environmentally responsible GenAI security practices. Recent surveys show that more than half of organizations lack a GenAI governance policy. Only 17% of organizations have clear, organization-wide guidelines. This gap gives proactive security teams a chance to step up.

Create an AI usage policy for employees

A good AI usage policy shows employees the right way to use GenAI at work. Your policy should cover:

• Approved AI tools and required permissions

• Data handling guidelines and confidentiality requirements

• Prohibited uses and potential disciplinary actions

• Training requirements for employees using GenAI tools

You should also update contracts or terms of service to limit liability from GenAI use, especially when you have to tell customers about these services.

Maintain an AI Bill of Materials (AI-BOM)

The AI Software Bill of Materials (AI-BOM) gives you a detailed inventory of all components in your GenAI systems. Global regulations are getting stricter, so keeping an accurate AI-BOM helps you stay compliant. A full AI-BOM should list:

• Model name, type, and version

• Developer information and contact details

• Training datasets and their limitations

• Software components and third-party dependencies

This documentation helps improve risk management, incident response, and supply chain security.

Use templates for risk assessments and audits

Make use of existing templates to simplify your GenAI risk assessment process. NIST’s GenAI Profile from July 2024 points out twelve specific risks of generative AI. The University of California AI Council’s Risk Assessment Guide provides extra frameworks that work well for administrative AI use.

Arrange with OWASP and NIST frameworks

These established frameworks give structure to your GenAI security program. The OWASP GenAI Security Project released key security guidance with the CISO Checklist in April 2024. NIST’s AI Risk Management Framework (AI RMF) gives you a voluntary way to handle AI-related risks. These frameworks help you spot and reduce risks while promoting secure and responsible AI deployment in various sectors.

Advanced DLP Takes Care of Your Data on Auto-pilot

GenAI security just needs smart solutions that can protect sensitive data without constant manual oversight. Advanced Data Loss Prevention (DLP) technology marks a major step forward to address this need.

Wald’s Advanced contextual engine delivers on accuracy and security

Wald Context Intelligence uses advanced AI models that redact sensitive information from prompts before they ever interact with any GenAI assistants.

This comprehensive approach works alongside user interactions to prevent data leakage and optimize workflows. The system redacts proprietary information, adds intelligent data substitutions for optimal AI model responses and repopulates the sensitive data before showing results to users.

Wald’s end-to-end encryption at every processing stage stands out as a unique feature that keeps your data secure throughout the workflow. Organizations retain complete control of their data logs with encrypted keys that only they can access.

Traditional DLP Solutions vs. Context Aware DLP (redaction, sanitization, false positives, negatives)

Traditional DLP tools don’t deal very well with today’s dynamic, unstructured data because they rely on rigid pattern-matching techniques:

• Pattern limitations: Most conventional systems use regular expressions, keyword matching, and bag-of-words models that lack contextual understanding

• False positive burden: Nearly 92% of enterprises think reducing DLP alert noise is “important” or “very important”

• Resource drain: Security teams waste time when they use traditional DLP to break down incidents, which creates operational inefficiencies

Context-aware DLP revolutionizes this space by understanding the meaning behind data rather than matching patterns. These advanced systems cut false positives by up to 10x compared to traditional regex-based tools. The improved accuracy leads to about 4x lower total cost of ownership.

Context-aware solutions excel at smart redaction that preserves document utility while protecting sensitive information. Contextual DLP tokenizes text with precision instead of over-redacting or missing sensitive data. This approach maintains compliance and preserves data utility.

Conclusion

A detailed approach addressing every stage of the AI lifecycle helps secure your GenAI systems. This guide has taught you to spot key security risks like prompt injection, data leakage, and model poisoning that put your AI investments at risk. On top of that, you now know why mapping your entire GenAI system lifecycle matters - from data collection through deployment and continuous monitoring.

The next rise in GenAI protection comes from context-aware DLP solutions that improve accuracy by a lot while reducing false positives compared to traditional approaches. These advanced systems protect sensitive data without affecting the productivity benefits that drove your GenAI adoption originally.

GenAI security must grow as the technology advances. Your organization should treat security as an ongoing process rather than a one-time implementation to get the most from generative AI while managing its unique risks effectively. The way you approach GenAI security today will shape how well your organization guides itself through the AI-powered future ahead.

FAQs

Q1. What are the key steps to secure a GenAI system?

Securing a GenAI system involves understanding risks like prompt injection and data leakage, implementing core security controls such as AI-specific DLP and role-based access, conducting regular testing through red teaming, and establishing governance frameworks aligned with industry standards like OWASP and NIST.

Q2. How can organizations protect sensitive data in GenAI applications?

Organizations can protect sensitive data by using advanced Data Loss Prevention (DLP) solutions that offer contextual understanding, implementing encryption protocols, applying role-based access controls, and maintaining an AI Bill of Materials (AI-BOM) to track all components of their GenAI systems.

Q3. What is the importance of data preparation in GenAI security?

Data preparation is crucial for GenAI security as it involves cleaning, formatting, and structuring data to make it suitable for use with GenAI models. This process helps in identifying and mitigating potential security vulnerabilities, ensuring data quality, and aligning with regulatory requirements.

Q4. How can companies monitor their GenAI systems for security threats?

Companies can monitor GenAI systems by implementing real-time monitoring tools for AI interactions, tracking token consumption through APIs, using anomaly detection algorithms to identify suspicious activity, and maintaining comprehensive audit logs of all AI decisions and outputs.

Q5. What role does governance play in GenAI security?

Governance plays a critical role in GenAI security by establishing clear usage policies for employees, maintaining documentation like the AI-BOM, conducting regular risk assessments, and ensuring alignment with established security frameworks. It provides the structure needed for long-term security compliance and responsible AI deployment.