AI Security Best Practices: A Practical Guide

As AI adoption grows, security becomes more complicated.

Traditional cybersecurity controls are still important, but AI systems introduce additional risks that organizations need to understand. An AI application may process confidential information, connect to internal databases, use external tools, retrieve documents, or make recommendations that influence business decisions.

Generative AI introduces risks such as prompt injection, sensitive information disclosure, supply-chain vulnerabilities, data and model poisoning, improper output handling, excessive agency, misinformation, and unbounded consumption. These risks are highlighted in the OWASP Top 10 for LLM and Generative AI applications.

AI security therefore requires more than simply protecting an AI model. Organizations need to secure the entire AI ecosystem, including data, applications, APIs, models, users, tools, infrastructure, and business processes.

What Is AI Security?

AI security refers to the practices used to protect artificial intelligence systems from unauthorized access, manipulation, misuse, data exposure, attacks, and unintended behavior.

It covers multiple layers:

• AI models

• Training and fine-tuning data

• Prompts and instructions

• APIs

• Vector databases

• Applications

• Connected tools

• User accounts

• Cloud infrastructure

• Monitoring systems

NIST's work on adversarial machine learning identifies different types of attacks, attacker goals, and stages of the machine-learning lifecycle, demonstrating that AI security needs to be considered throughout the system lifecycle.

The goal is not to make AI completely risk-free. Instead, the objective is to identify realistic threats and establish controls that reduce their likelihood and impact.

Why AI Security Matters

An AI application can have access to information and systems that were previously separated.

Consider an AI customer-service assistant connected to:

• Customer records

• Order databases

• Internal documentation

• Email systems

• CRM software

• Business APIs

If the AI application is poorly secured, an attacker may attempt to manipulate the model into revealing information or taking actions beyond what the user should be allowed to perform.

This is why AI security needs to consider both traditional application security and AI-specific attack patterns.

A secure AI system should protect:

Data → Model → Application → Tools → Users → Infrastructure

Major AI Security Risks

1. Prompt Injection

Prompt injection is one of the most important security concerns for LLM applications.

An attacker can provide instructions designed to change how the AI behaves or bypass intended restrictions.

For example, a customer-support assistant may be instructed to ignore its normal purpose and reveal information that should remain confidential.

OWASP identifies prompt injection as LLM01:2025 and recommends controls including input and output filtering, least-privilege access, human approval for high-risk actions, separation of untrusted content, and adversarial testing.

The important lesson is that prompts should not be treated as a complete security boundary.

2. Sensitive Information Disclosure

AI applications may process sensitive information such as:

• Customer records

• Financial data

• Passwords

• Business documents

• Personal information

• Internal source code

• Confidential strategies

If access controls are weak, sensitive information may appear in AI responses or application logs.

Organizations should therefore apply normal data-security principles to AI systems, including access control, data minimization, encryption where appropriate, monitoring, and secure handling of sensitive information.

OWASP specifically lists sensitive information disclosure among its major LLM application risks and provides dedicated guidance for LLM data security.

3. Supply-Chain Risks

Modern AI applications often depend on external components.

These may include:

• Foundation models

• Open-source libraries

• APIs

• Plugins

• Datasets

• Embedding models

• Vector databases

• Cloud services

A vulnerability in one component can affect the wider AI application.

Organizations should therefore evaluate AI vendors and third-party components before integrating them into production systems.

4. Data and Model Poisoning

Training, fine-tuning, and embedding data can influence AI behavior.

If attackers manipulate data used during these processes, they may attempt to introduce unwanted behavior, vulnerabilities, or biases.

OWASP's 2025 LLM guidance identifies data and model poisoning as a specific security risk.

Organizations should establish controls around:

• Dataset sources

• Data validation

• Data provenance

• Access permissions

• Change tracking

• Training pipelines

5. Excessive Agency

AI systems are becoming increasingly capable of calling external tools and performing actions.

For example, an AI agent may be able to:

• Send emails

• Create tickets

• Update records

• Query databases

• Execute workflows

• Call APIs

The more authority an AI system has, the greater the potential impact of an error or attack.

OWASP identifies excessive agency as LLM06:2025 and recommends reducing unnecessary functionality, permissions, and autonomy.

A strong principle is:

Give the AI only the permissions it actually needs.

1. Apply Least-Privilege Access

AI applications should operate with the minimum permissions necessary.

For example, if an AI assistant only needs read access to a knowledge base, it should not receive permission to modify or delete records.

Use separate credentials and permissions for AI applications rather than sharing powerful administrative credentials.

OWASP specifically recommends least-privilege controls for reducing prompt-injection and excessive-agency risks.

2. Keep Data and Instructions Separate

One major challenge with LLM applications is that natural-language instructions and external content can be processed together.

External documents, web pages, emails, or uploaded files may contain instructions that attempt to manipulate the AI.

Applications should therefore clearly distinguish between:

Trusted instructions

and

Untrusted data

This is particularly important for RAG applications and AI agents that retrieve information from external sources.

3. Validate AI Outputs

AI-generated output should not automatically be trusted by downstream systems.

Suppose an AI application generates a database query, API parameter, HTML fragment, or command.

Passing that output directly to another system can create security vulnerabilities.

OWASP recommends validating and appropriately handling model output before it reaches backend functions or other systems.

A safer workflow is:

AI output → Validation → Authorization check → Sanitization → Action

rather than:

AI output → Automatic execution

4. Protect API Keys and Secrets

Never place passwords, API keys, database credentials, or private tokens inside prompts or system instructions.

OWASP specifically warns that system prompts should not be treated as secret storage and that credentials and connection strings should not be embedded within them.

Use appropriate secret-management mechanisms instead.

This principle applies to:

• API keys

• Cloud credentials

• Database passwords

• Authentication tokens

• Encryption keys

• Service credentials

5. Secure RAG and Vector Databases

Retrieval-Augmented Generation systems introduce additional security considerations.

A RAG application may retrieve information from a vector database before generating a response.

Security controls should consider:

• Who can upload documents?

• Who can retrieve documents?

• How are documents classified?

• Are access permissions preserved during retrieval?

• Can one user retrieve another user's information?

• Are embeddings protected?

• Are documents validated before indexing?

OWASP identifies vector and embedding weaknesses as LLM08:2025, showing that security must extend beyond the language model itself.

6. Monitor AI Activity

Security monitoring should cover AI-specific events as well as traditional infrastructure events.

Track activities such as:

• Unusual prompt patterns

• Repeated failed requests

• Unexpected tool calls

• Sensitive-data access

• Large volumes of requests

• Abnormal API usage

• Model errors

• Unauthorized actions

Monitoring can help security teams identify suspicious behavior earlier.

Logging should also be designed carefully so that security monitoring does not unintentionally expose sensitive information.

7. Conduct AI Red Teaming

Security testing should attempt to discover how an AI system behaves under hostile conditions.

Test scenarios can include:

• Prompt injection

• Jailbreak attempts

• Sensitive-data extraction

• Malicious documents

• Unauthorized tool use

• Excessive permissions

• RAG manipulation

• Unexpected model outputs

CISA has emphasized secure-by-design practices for AI systems, including vulnerability management, testing, and red-team activities.

AI red teaming should be conducted before production and repeated as systems change.

8. Secure the AI Supply Chain

Organizations should maintain visibility into the components used by their AI applications.

Before adopting an external AI service, consider:

• Vendor security practices

• Data-processing policies

• Model provenance

• Software dependencies

• Security update processes

• Incident response

• Access controls

• Service availability

A secure AI application can still become vulnerable if an important third-party dependency is compromised.

9. Train Employees

Employees are an important part of AI security.

Training should explain:

• What information can be entered into AI tools

• Which AI applications are approved

• How to identify suspicious AI behavior

• Why confidential information should be protected

• How prompt injection works

• How to report security incidents

• Why AI outputs require verification

Organizations should provide practical examples rather than relying only on lengthy security policies.

10. Build Security Into the AI Lifecycle

AI security should begin before deployment.

A practical lifecycle looks like:

Plan → Design → Develop → Test → Deploy → Monitor → Improve → Retire

Security should be evaluated at each stage.

For example:

Planning

Identify the AI use case and potential risks.

Development

Apply secure coding and data-management practices.

Testing

Perform security, accuracy, and adversarial testing.

Deployment

Use appropriate access controls and monitoring.

Operations

Continuously monitor behavior and vulnerabilities.

Retirement

Securely remove data, credentials, models, and connected resources when the system is no longer needed.

This approach aligns with the broader security principle of building security into technology from the beginning rather than attempting to add it afterward. CISA's AI roadmap specifically called for integrating AI security into its Secure by Design initiative.

AI Security Checklist

Before deploying an AI application, organizations can ask:

• Is the AI use case clearly defined?

• What data does the system process?

• Who can access it?

• What permissions does the AI have?

• Can users influence its behavior through external content?

• Are prompts and data properly separated?

• Are model outputs validated?

• Are sensitive credentials protected?

• Is the AI connected to external tools?

• Are high-risk actions subject to human approval?

• Has adversarial testing been completed?

• Are logs and monitoring enabled?

• Is the AI vendor trustworthy?

• Is there an incident-response process?

• Are employees trained?

If several of these questions cannot be answered, the system may not yet be ready for production.

The Future of AI Security

AI security will become increasingly important as organizations move from simple chatbots toward AI agents and automated workflows.

An AI system that only generates text presents one type of risk.

An AI system that can access databases, send messages, modify records, or execute workflows introduces a much larger security surface.

NIST's ongoing work on adversarial machine learning and AI cybersecurity reflects the growing need to address threats across the AI lifecycle.

Future AI security will therefore involve a combination of:

• Traditional cybersecurity

• Identity and access management

• Data security

• Application security

• Model security

• AI-specific testing

• Governance

• Continuous monitoring

Organizations that treat AI security as part of their overall security strategy will be better positioned to adopt AI responsibly.

Conclusion

AI can deliver significant business value, but secure implementation requires more than choosing a reliable AI model.

Organizations need to protect the entire AI ecosystem, including data, models, applications, APIs, users, external tools, and infrastructure.

The most important practices include least-privilege access, secure data handling, output validation, secret protection, RAG security, continuous monitoring, AI red teaming, supply-chain management, employee training, and lifecycle-based security.

OWASP's current LLM guidance provides a useful starting point for understanding AI-specific application risks, while NIST's adversarial machine-learning work provides broader terminology and a structured view of attacks and mitigations.

The key principle is straightforward:

AI should be powerful enough to be useful, but restricted enough to remain controllable.

As AI becomes more deeply integrated into business processes, professionals who understand both AI capabilities and security principles will become increasingly valuable.

Why Learn AI Security with Nevolearn?

AI security is becoming an essential skill as organizations introduce Generative AI, automation, AI agents, and intelligent applications into their workflows.

Professionals need more than the ability to operate AI tools. They also need to understand security risks, responsible AI practices, data protection, prompt injection, access control, AI governance, and safe automation.

Nevolearn helps professionals build practical, future-ready skills across Artificial Intelligence, Generative AI, AI Automation, AI Agents, Prompt Engineering, Machine Learning, Data Analytics, Project Management, and Digital Transformation.

Learning these areas together can help professionals understand both the opportunities and limitations of modern AI systems.

For technology professionals, project managers, business analysts, developers, and aspiring AI specialists, AI security knowledge can provide an important advantage when working with 

AI-powered systems.

Build practical AI skills and learn to use emerging technologies more securely with Nevolearn.