AI Security|Risks, Threats, and Best Practices for 2026

AI security is the practice of protecting artificial intelligence systems, models, training data, applications, and users from cyberattacks, unauthorized access, manipulation, and data exposure. It combines traditional cybersecurity controls with AI-specific safeguards, including prompt-injection testing, model access restrictions, output validation, continuous monitoring, and human oversight.

AI Security at a Glance

• Definition: Protecting AI systems and the data, infrastructure, and applications they depend on.
• Major threats: Prompt injection, sensitive data exposure, model poisoning, insecure integrations, and excessive AI-agent permissions.
• Core defenses: Strong identity controls, data protection, secure development, testing, monitoring, and incident response.
• Key frameworks: NIST’s AI Risk Management Framework and OWASP’s GenAI security guidance.
• Business priority: Secure the complete AI workflow, not just the model.
• Important limitation: No single security tool or certification eliminates every AI-related risk.

What Is AI Security?

AI security refers to the technologies, processes, and policies used to protect artificial intelligence systems throughout their lifecycle. It covers everything from model development and data preparation to deployment, application integration, monitoring, and retirement.

Traditional cybersecurity focuses on protecting systems, networks, applications, and information. AI security includes those responsibilities but also addresses vulnerabilities created by model behavior, machine learning pipelines, generative AI, and autonomous agents.

For example, a company might deploy an AI assistant that searches internal documents and answers employee questions. The system needs secure authentication, appropriate document permissions, protected API connections, and safeguards against malicious instructions embedded in retrieved content.

Without these controls, an assistant could expose information that a user should not access, even if the underlying language model functions as designed.

AI security therefore involves two connected objectives:

• Securing AI: Preventing attackers from compromising models, training pipelines, AI applications, or supporting infrastructure.
• Using AI securely: Preventing AI-enabled applications from exposing data, making unauthorized changes, or creating new security weaknesses.

The distinction matters because a model can operate correctly while its surrounding application remains vulnerable.

The National Institute of Standards and Technology (NIST) identifies security and resilience as important characteristics of trustworthy AI. Its AI Risk Management Framework provides a structured approach to identifying, assessing, and managing risks throughout the AI lifecycle. Read the NIST AI Risk Management Framework.

Why Is AI Security Important?

Organizations increasingly use AI to analyze documents, generate content, write code, answer customer questions, and automate workflows. These capabilities create new opportunities, but they also expand the number of systems and data flows that security teams must manage.

An AI application may connect a language model to a company database, a third-party API, a document repository, and an automated workflow. Each connection introduces potential security concerns.

Protecting sensitive information

AI tools may process customer records, internal reports, source code, financial information, and personal data. Poor access controls or unclear data-handling practices can expose this information.

Businesses should understand what data enters an AI system, where it goes, how long it remains available, and who can access it.

Preventing unauthorized actions

AI agents can perform tasks beyond generating text. Depending on their permissions, they may create support tickets, modify files, query databases, or trigger external services.

Excessive permissions increase the consequences of a compromised or manipulated agent. Restricting access and requiring approval for sensitive actions can reduce that risk.

Maintaining reliable operations

Attackers may exploit AI infrastructure to disrupt services, consume expensive computing resources, or interfere with model outputs.

Security controls help organizations protect availability, maintain operational continuity, and detect unusual behavior before incidents escalate.

Meeting governance requirements

Organizations may face privacy, contractual, industry-specific, or legal obligations when they use AI. The exact requirements depend on their jurisdiction, sector, data, and intended use.

A documented security and governance process helps teams identify responsibilities and demonstrate how they manage risk.

How Does AI Security Work?

AI security works through multiple layers of protection. No single layer is sufficient because vulnerabilities can appear in the model, data, application code, infrastructure, or human workflow.

1. Identify AI assets and data flows

Start by documenting every AI system used by the organization. Include approved applications, internal models, third-party APIs, AI agents, datasets, and employee use of external services.

Map how information moves between these components. Identify sensitive data, external connections, and actions that an AI system can perform.

This process also helps reveal unauthorized AI usage, sometimes called shadow AI, where employees use tools without formal organizational approval.

2. Establish access controls

Use identity verification and role-based permissions to control who can access AI applications, datasets, model endpoints, and administrative functions.

Apply the principle of least privilege: each user, service, and agent should receive only the permissions necessary for its task.

For an AI assistant connected to company documents, enforce access permissions at the retrieval layer. Do not rely on the model to decide whether a user is authorized to see a document.

3. Protect data and models

Encrypt sensitive information in transit and at rest where appropriate. Manage secrets securely, restrict access to model artifacts, and monitor changes to training datasets and configurations.

Organizations using third-party AI services should also review data retention, model-training terms, subprocessors, and contractual protections.

4. Test for AI-specific attacks

Test AI applications with adversarial inputs, malicious documents, unauthorized access attempts, and unexpected tool requests.

Evaluate whether the application can resist prompt injection, protect sensitive information, validate outputs, and prevent unauthorized actions.

5. Monitor and respond

Collect relevant application logs, authentication events, API activity, tool calls, and security alerts. Establish incident-response procedures that cover both conventional cyberattacks and AI-specific failures.

After deployment, continue testing because model updates, new integrations, changing datasets, and evolving attack techniques can introduce fresh vulnerabilities.

Major AI Security Threats

AI security involves traditional cybersecurity weaknesses as well as attacks that exploit the behavior of machine learning systems.

OWASP’s GenAI Security Project provides guidance on common risks in large language model applications. Its 2026 release is the current version listed on the project’s official site. Explore the OWASP GenAI LLM Top 10. https://genai.owasp.org/llm-top-10/

Prompt injection

Prompt injection occurs when malicious instructions influence an AI system in ways that conflict with its intended task or security boundaries.

For example, an attacker might place instructions inside a document that an AI assistant retrieves. The instructions could attempt to make the assistant disclose information or misuse an available tool.

Defenses include separating trusted instructions from untrusted content, limiting tool permissions, validating actions outside the model, and testing realistic attack scenarios.

There is no universal prompt that eliminates every prompt-injection risk. Applications need security controls that remain effective even when the model is manipulated.

Sensitive information disclosure

An AI system may reveal information through its responses, logs, retrieval results, or integrations.

This risk can arise from excessive permissions, poorly configured data retrieval, accidental disclosure by users, or inadequate data handling by a provider.

Reduce exposure by minimizing sensitive inputs, enforcing access controls, reviewing retention policies, and testing whether unauthorized users can retrieve restricted information.

Data and model poisoning

Data poisoning involves manipulating training, fine-tuning, or other data used by an AI system to influence its behavior. Attackers may also target data sources used in retrieval-augmented generation systems.

Organizations should verify data provenance, restrict changes to datasets, review suspicious records, and monitor model behavior after updates.

For third-party models, examine available documentation and the provider’s process for reporting vulnerabilities.

Supply-chain vulnerabilities

AI systems rely on external components such as model packages, datasets, libraries, containers, cloud services, and APIs.

A compromised dependency or untrusted model artifact can introduce vulnerabilities before an application reaches production.

Use trusted sources, pin and verify dependencies, scan software components, restrict deployment permissions, and maintain an inventory of models and related assets.

Insecure output handling

AI-generated text or structured output may be passed to another system. If the application trusts that output without validation, it can create security problems.

For example, an application might incorrectly treat model-generated content as safe SQL, executable code, HTML, or a command.

Validate outputs using appropriate rules and established security controls. Never treat generated content as inherently trustworthy.

Excessive AI-agent permissions

AI agents can combine model reasoning with access to external tools. This can make them useful, but it also increases the impact of errors or manipulation.

An agent that can read documents may not need permission to delete them. An assistant that drafts payments should not automatically receive unrestricted authority to execute transactions.

Use narrow permissions, explicit approval gates, transaction limits, and logs of consequential actions.

Denial of service and unexpected costs

AI systems may consume substantial computing resources when processing large inputs or repeated requests. Attackers or poorly controlled workflows can drive up costs or reduce service availability.

Apply rate limits, request quotas, input-size restrictions, budget alerts, and appropriate capacity controls.

Best Practices for AI Security

A practical AI security program combines existing cybersecurity fundamentals with controls designed for AI-specific risks.

Build security into development

Adopt secure development practices from the start. Review architecture, dependencies, authentication, authorization, data handling, and deployment configuration before release.

NIST’s AI Risk Management Framework can help teams organize risk-management activities across design, development, deployment, and evaluation. Its generative AI profile provides additional guidance for risks associated with generative systems. Read the NIST Generative AI Profile.

Apply least privilege to models and agents

Limit access to databases, files, APIs, and administrative functions. Use separate service identities where appropriate and keep credentials out of prompts and model-visible content.

Require human approval for high-impact actions, such as deleting records, transferring money, changing access permissions, or sending sensitive information externally.

Protect training and retrieval data

Maintain data inventories and establish rules for collection, validation, access, retention, and deletion.

For retrieval-augmented generation (RAG), enforce permissions before documents reach the model. RAG allows a system to retrieve external information at response time, but it does not automatically make that information trustworthy or safe.

Test with realistic adversarial scenarios

Security testing should cover more than ordinary prompts and expected outputs.

Test malicious documents, indirect prompt injection, unauthorized tool calls, data leakage, malformed inputs, and attempts to bypass access restrictions.

Repeat testing after major model changes, application updates, or new integrations.

Monitor behavior after deployment

Track unusual access patterns, repeated failures, unexpected tool use, resource consumption, and changes in output quality.

Create clear escalation procedures for suspected incidents. Depending on the application, teams may need to disable a tool, revoke credentials, isolate a service, or temporarily suspend an agent.

Train employees on safe AI use

Provide clear guidance on approved tools, confidential data, external sharing, output verification, and incident reporting.

A blanket ban may push usage into less visible channels. Organizations should define prohibited uses while offering approved tools and workable alternatives.

AI Security Tools and Frameworks

AI security includes several categories of products and resources. They serve different purposes, so organizations should select them according to their architecture and risk profile.

Tool or framework	Target user	Key strength	Pricing transparency	Core limitation
NIST AI Risk Management Framework	Security leaders, risk teams, AI developers	Structured AI risk governance	Public framework available free	Guidance does not automatically implement controls
OWASP GenAI Security Project	Developers, penetration testers, security teams	AI application threat guidance and risk categories	Public guidance available free	Requires technical adaptation to each application
Microsoft Security	Enterprise security and IT teams	Security capabilities across supported Microsoft environments	Product and licensing dependent	Coverage depends on selected products and configuration
Google Cloud Security	Cloud teams and AI developers	Security controls for supported cloud workloads	Product and usage dependent	Requires careful configuration and architecture-specific review
AWS Security services	Cloud engineers and enterprise teams	Security capabilities for supported AWS environments	Product and usage dependent	Does not remove application-level AI risks

The NIST and OWASP resources are frameworks and guidance, not substitutes for technical controls. Vendor platforms may provide useful security features, but the presence of a product does not prove that a particular AI deployment is secure.

Before buying an AI security product, determine which risks it covers, what data it can inspect, how it integrates with your stack, and what evidence supports its effectiveness.

AI Security vs. Traditional Cybersecurity

AI security builds on traditional cybersecurity rather than replacing it.

Area	Traditional cybersecurity	AI security considerations
Identity and access	Authenticate users and control permissions	Also restrict model tools, agents, and retrieval access
Data protection	Protect stored and transmitted information	Prevent sensitive data from entering or leaking through AI workflows
Application security	Find and fix software vulnerabilities	Test model interactions, output handling, and AI integrations
Threat detection	Monitor systems, accounts, and networks	Also examine unusual prompts, tool calls, and model behavior
Testing	Test code, infrastructure, and configurations	Include adversarial model testing and AI-specific abuse cases
Governance	Assign risk ownership and policies	Address model provenance, AI use cases, oversight, and model changes

The strongest approach combines both disciplines. A secure model running inside an insecure application can still expose data or enable unauthorized actions.

Likewise, an application with strong conventional security controls may remain vulnerable if its AI component can be manipulated into making unsafe decisions.

AI Security and Data Privacy

AI security and data privacy overlap, but they are not identical.

Security focuses on preventing unauthorized access, misuse, disruption, and compromise. Privacy focuses on how personal information is collected, processed, shared, retained, and deleted.

A system can use encryption and strong authentication yet still collect more personal information than necessary. Conversely, a privacy policy cannot compensate for weak access controls.

Review AI provider data policies

Before sending information to a third-party AI service, determine:

• Whether submitted content may be used to improve or train models.
• How long prompts, outputs, and logs are retained.
• Whether the provider shares data with subprocessors.
• What deletion options are available.
• Where data is processed and which contractual terms apply.
• Whether enterprise controls differ from consumer accounts.

Do not assume that every provider follows the same rules. Policies may differ by product, account type, region, and contract.

Understand SOC 2 and zero data retention

SOC 2 is an independent examination framework for controls relevant to areas such as security and availability. A SOC 2 report can provide useful evidence about a provider’s controls, but its scope and findings matter.

SOC 2 does not automatically mean zero data retention. Zero data retention, often abbreviated ZDR, refers to specific arrangements for retaining or storing submitted data. Verify the actual terms and technical scope instead of relying on a general compliance claim.

AI Security for Businesses: A Practical Checklist

Organizations do not need to solve every AI security challenge at once. Start with the systems that handle sensitive data, support important operations, or have permission to take consequential actions.

Use this checklist to structure an initial review.

• Inventory approved AI tools, models, agents, and integrations.
• Classify the data each system can access.
• Identify external APIs and third-party dependencies.
• Enforce authentication and least-privilege access.
• Review provider retention and training policies.
• Test prompt injection and unauthorized data retrieval.
• Validate model outputs before using them in sensitive workflows.
• Apply rate limits and monitor usage costs.
• Require approval for high-impact agent actions.
• Log relevant activity and define incident-response procedures.
• Train employees on acceptable AI use.
• Repeat assessments after significant changes.

Assign an owner to each control and document how the organization will verify it. A checklist is useful only when teams can demonstrate that the safeguards work.

The Future of AI Security

AI security is evolving as organizations move from simple chat interfaces to systems that retrieve private data, use tools, and complete multi-step tasks.

This shift changes the security boundary. A text-generation system mainly produces responses, while an agent may interact with databases, browsers, software repositories, or business applications.

Security teams must therefore assess not only what a model can say, but also what the surrounding system allows it to do.

Three developments deserve particular attention.

Agent security and delegated authority

As organizations deploy more capable agents, permission design and action validation become increasingly important. Teams need clear limits on delegated authority, reliable audit trails, and controls for sensitive operations.

AI supply-chain transparency

Model weights, training data, dependencies, and external services can all affect system security. Better inventories and provenance checks can help organizations understand what they deploy and where risks may originate.

Continuous evaluation

AI behavior can change when developers update models, modify prompts, change retrieval sources, or add tools. Security testing should therefore continue throughout deployment rather than end at launch.

NIST continues to develop resources for AI risk management, including work on trustworthy AI in critical infrastructure. Its AI Resource Center provides technical guidance and evaluation resources for organizations putting the framework into practice. Explore the NIST AI Resource Center.

These developments reinforce one practical principle: AI security is an ongoing operational responsibility, not a one-time product purchase.

Frequently Asked Questions

1. What is AI security in simple terms?

AI security means protecting artificial intelligence systems, their data, and their connected applications from attacks, unauthorized access, manipulation, and misuse. It combines traditional cybersecurity practices with safeguards designed for machine learning models, generative AI, and AI agents.

2. What are the biggest AI security risks?

Major risks include prompt injection, sensitive information disclosure, data and model poisoning, insecure integrations, supply-chain vulnerabilities, and excessive agent permissions. The most relevant threats depend on the system’s design, data access, deployment environment, and available tools.

3. How can businesses improve AI security?

Businesses can improve AI security by inventorying AI systems, enforcing least-privilege access, protecting sensitive data, testing for adversarial inputs, monitoring usage, and validating model outputs. They should also review provider contracts, train employees, and require human approval for high-impact actions.

4. What is the difference between AI security and AI safety?

AI security focuses on protecting systems and data from attacks, compromise, and unauthorized actions. AI safety focuses more broadly on preventing harmful behavior and unintended consequences. The fields overlap, especially when a system’s unsafe behavior could create security or operational risks.

5. Which AI security frameworks should organizations consider?

Organizations can consider NIST’s AI Risk Management Framework for structured risk management and OWASP’s GenAI security guidance for application-level threats. These resources serve different purposes and should be adapted to the organization’s technology, risk tolerance, and regulatory obligations.

Conclusion

AI security protects models, data, applications, and users against both conventional cyber threats and risks specific to AI systems. Effective protection requires layered controls, careful data governance, realistic testing, restricted agent permissions, and ongoing monitoring.

Businesses, developers, and security teams should start by mapping their AI workflows and identifying the most consequential risks. Frameworks such as NIST AI RMF and OWASP GenAI guidance can help structure that work, but practical security depends on how well controls are implemented and tested.

The goal is not to eliminate every possible risk. It is to reduce exposure, detect failures quickly, and ensure that AI systems operate within clearly defined security boundaries.