What Is AI Security? A Beginner’s Guide for Businesses in 2026

What Is AI Security? A Beginner’s Guide for Businesses in 2026

Artificial intelligence is becoming part of everyday business operations. Organizations are using AI to analyze data, automate processes, assist employees, improve customer experiences, and build new products.

But as AI becomes connected to business data, applications, APIs, and internal systems, it also creates new security challenges.

AI security is the practice of protecting AI models, applications, data, integrations, and users from security threats and unintended behavior.

For businesses, AI security is not only about protecting the AI model itself. It is also about controlling what information an AI system can access, what actions it can perform, and how its outputs are used.

This guide explains what AI security means, how it differs from traditional cybersecurity, the major risks organizations should understand in 2026, and practical steps businesses can take to build a stronger AI security program.

What Is AI Security?

AI security refers to the technologies, processes, controls, and practices used to protect artificial intelligence systems throughout their lifecycle.

This can include:

• AI models and machine-learning systems
• Generative AI and large language model (LLM) applications
• Training and retrieval data
• APIs and integrations
• AI agents and automated workflows
• Vector databases and embeddings
• Applications that use AI-generated output
• Users and administrators interacting with AI systems

The goal is to prevent unauthorized access, manipulation, data exposure, misuse, and other security problems.

The National Institute of Standards and Technology (NIST) provides the AI Risk Management Framework (AI RMF) as a voluntary resource for organizations managing AI risks. NIST also published a Generative AI Profile to help organizations identify and manage risks associated with generative AI.

AI Security vs. Cybersecurity: What’s the Difference?

AI security and cybersecurity overlap, but they are not exactly the same.

Cybersecurity generally focuses on protecting systems, networks, applications, devices, identities, and data from cyber threats.

AI security applies security principles to AI-specific components and also addresses risks that arise because AI systems can interpret instructions, generate content, retrieve information, and sometimes take actions through connected tools.

For example, a traditional application may have a vulnerability that allows unauthorized access.

An AI application could have an additional problem: a malicious instruction embedded in a document or webpage might influence the model’s behavior when that content is processed.

This is one reason organizations need to consider AI security as part of their broader cybersecurity strategy rather than treating it as an isolated technology issue.

Why Does AI Security Matter in 2026?

The security implications of AI become more significant as organizations connect AI systems to real business environments.

An AI assistant may have access to internal documents. An AI-powered application may retrieve information from databases. An AI agent may interact with APIs or other software.

The more access and authority an AI system has, the more important security controls become.

NIST’s Generative AI Profile identifies risks associated with generative AI and provides risk-management actions organizations can consider throughout the AI lifecycle.

Meanwhile, OWASP’s 2025 Top 10 for LLM and Generative AI applications identifies risks including prompt injection, sensitive information disclosure, supply-chain vulnerabilities, data and model poisoning, excessive agency, vector and embedding weaknesses, misinformation, and unbounded consumption.

7 Major AI Security Risks Businesses Should Understand

1. Prompt Injection

Prompt injection occurs when specially crafted instructions influence an AI system’s behavior in unintended ways.

An attacker might place malicious instructions inside a user prompt, document, webpage, email, image, or other content that an AI system processes.

The potential impact depends heavily on the application and the permissions available to the AI system.

According to OWASP, prompt injection can potentially contribute to sensitive information disclosure, unauthorized functions, arbitrary commands in connected systems, and manipulation of decision-making processes.

Example

Imagine an AI assistant that can search an organization’s internal knowledge base.

An attacker submits a document containing hidden instructions telling the assistant to ignore its normal instructions and reveal information from another source.

If the application’s security architecture does not properly isolate instructions, data, and permissions, the model may produce an unintended response.

How businesses can reduce the risk

Organizations should consider:

• Separating trusted instructions from untrusted content
• Limiting the AI application’s permissions
• Validating inputs and outputs
• Monitoring unusual model behavior
• Testing applications against prompt-injection scenarios
• Keeping sensitive operations behind independent authorization controls

2. Sensitive Information Disclosure

AI applications may interact with sensitive business information such as customer records, financial information, credentials, internal documents, source code, or personally identifiable information.

If these systems are poorly configured, sensitive information could be exposed through prompts, retrieval systems, logs, integrations, or generated responses.

OWASP specifically identifies sensitive information disclosure as an LLM application risk and notes that exposed information can include personal data, financial details, confidential business information, credentials, and legal documents.

Practical controls

Businesses should:

• Classify information before allowing AI access
• Apply data-loss-prevention controls where appropriate
• Restrict access based on user identity and role
• Avoid putting credentials or secrets into prompts
• Review what AI applications retain and log
• Monitor data flows between AI applications and other systems

3. AI Supply-Chain Risk

AI systems rarely operate in isolation.

A modern AI application may depend on:

• Third-party models
• Open-source libraries
• APIs
• Plugins
• Datasets
• Model repositories
• Embedding models
• External AI services

Each dependency can introduce additional security and operational risk.

OWASP includes Supply Chain among its 2025 LLM application risks.

Businesses should therefore evaluate the components that make up an AI application rather than focusing only on the model itself.

4. Data and Model Poisoning

AI systems depend heavily on data.

If malicious or unreliable data enters a training, fine-tuning, retrieval, or embedding pipeline, it can affect the behavior or reliability of the resulting system.

This is known as data or model poisoning.

OWASP lists Data and Model Poisoning as one of its 2025 LLM application risks.

• Businesses should consider
• Where training and retrieval data comes from
• Who can modify datasets
• How datasets are validated
• Whether changes are logged
• How models and embeddings are versioned
• Whether unexpected model behavior can be traced back to data changes

5. Excessive AI Agency

Some AI applications can do more than answer questions.

They can potentially:

• Send emails
• Create records
• Call APIs
• Search databases
• Modify documents
• Execute workflows
• Trigger business processes

Giving an AI system excessive permissions can increase the impact of an error, compromised instruction, or malicious input.

OWASP identifies Excessive Agency as a specific LLM application security risk.

A useful principle is simple:

Give an AI system only the permissions it actually needs.

This is similar to the principle of least privilege used in traditional cybersecurity.

6. Vector and Embedding Weaknesses

Many modern AI applications use retrieval-augmented generation (RAG).

Instead of relying entirely on information stored within a model, the application retrieves relevant information from an external knowledge base and provides it to the model.

This often involves embeddings and vector databases.

These systems introduce additional security considerations, including:

• Unauthorized access to stored information
• Incorrect data retrieval
• Cross-user data exposure
• Weak access controls
• Manipulated documents
• Poor separation between different data sources

OWASP includes Vector and Embedding Weaknesses in its 2025 LLM risk list.

7. AI-Generated Errors and Misinformation

AI systems can produce convincing but incorrect information.

In a business environment, this can become a security and operational problem when AI-generated content is automatically trusted or used to make decisions.

For example, an AI system could produce:

• Incorrect technical instructions
• Fabricated references
• Incorrect financial information
• Incorrect security recommendations
• Misleading summaries
• Invalid code

OWASP’s 2025 LLM risk list includes Misinformation as a specific risk.

The solution is not necessarily to eliminate AI use. Instead, organizations should determine where human review, validation, testing, and additional controls are required.

How Businesses Can Build an AI Security Strategy

AI security does not need to begin with a complicated technology stack.

Organizations can start with a structured approach.

1. Know Where AI Is Being Used

Create an inventory of AI applications across the organization.

Look beyond officially approved tools.

Employees may be using public AI assistants, coding assistants, document-processing tools, or AI-enabled features inside existing software.

Understanding the actual AI environment is the first step toward managing it.

2. Classify the Data

Determine what information AI systems can access.

For example:

• Public information
• Internal information
• Confidential business information
• Customer information
• Financial information
• Credentials and secrets

Not every AI system should have access to every category of information.

3. Apply Least Privilege

AI applications should receive only the permissions required for their intended purpose.

If an AI assistant only needs read access to a specific knowledge base, it should not automatically receive permission to modify databases or execute unrelated business processes.

4. Secure the AI Development Lifecycle

Security should be considered during:

Design → Development → Testing → Deployment → Monitoring → Retirement

Security testing should cover both traditional application vulnerabilities and AI-specific risks.

Organizations can use frameworks such as the NIST AI RMF and security guidance from OWASP as references when developing their approach.

5. Monitor AI Applications

Security teams should monitor AI applications just as they monitor other important technology systems.

Depending on the application, useful signals may include:

• Unusual prompts
• Repeated failed requests
• Unexpected data access
• Abnormal API activity
• Excessive usage
• Unexpected model outputs
• Changes to AI configurations
• Suspicious tool or agent activity

Monitoring helps organizations identify problems after deployment rather than relying entirely on preventive controls.

6. Establish AI Governance

Technology controls are only part of the solution.

Organizations should also define policies covering:

• Approved AI tools
• Acceptable data usage
• Sensitive information
• Human review
• AI-generated content
• Third-party AI services
• Security testing
• Incident response
• Vendor management

NIST’s AI RMF provides a structured framework organizations can use when developing AI risk-management practices.

A Simple AI Security Checklist

Before deploying an AI application, ask:

Governance

• Do we know what the AI application is supposed to do?
• Who owns the application?
• What policies apply to it?

Data

• What information can the AI access?
• Is sensitive information protected?
• Are data sources trustworthy?

Access

• What permissions does the AI have?
• Are those permissions necessary?
• Are privileged actions independently authorized?

Application Security

• Have we tested for prompt injection?
• Are AI outputs validated?
• Are APIs and integrations protected?

Monitoring

• Are AI interactions logged appropriately?
• Can unusual activity be detected?
• Do we have an incident-response process?

Human Oversight

• Which decisions require human approval?
• Can the AI perform high-impact actions without review?
• Are users trained to recognize AI-generated errors?

The Future of AI Security

AI security will continue to evolve as organizations move from simple AI chatbots toward applications that can retrieve information, interact with enterprise systems, and perform actions through tools and agents.

This means the security conversation will increasingly involve more than model protection.

It will include:

• Identity and access management
• Data security
• Application security
• Cloud security
• AI governance
• Supply-chain security
• Monitoring and observability
• Human oversight
• Security testing

The organizations that approach AI security as part of their broader security and risk-management program will be better positioned to understand how AI is being used and where additional controls are needed.

Final Takeaway

AI can create significant business value, but introducing AI into an organization also introduces new security considerations.

AI security is about protecting the entire AI ecosystem – including models, applications, data, integrations, users, and automated actions.

For businesses beginning their AI security journey, the fundamentals are straightforward:

Know where AI is being used. Protect the data. Limit permissions. Test AI applications. Monitor activity. Establish governance.

AI security is not a one-time project. As AI applications evolve, security controls and governance should evolve with them.

Further Reading

NIST — Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile

NIST — AI Risk Management Framework

OWASP — Top 10 for LLM and Generative AI Applications

OWASP — LLM01:2025 Prompt Injection

OWASP — LLM02:2025 Sensitive Information Disclosure