Enterprises racing to deploy AI are running two clocks at once. One measures how fast a model ships, while the other measures how fast someone finds a way to break it. And lately, the second clock is winning. Attackers are folding AI into their own playbooks just as fast as defenders are folding AI into theirs.
In our last article, we discussed what we learned from an AI Risk plan we walked through. In this piece, we will take another step towards AI Security.
AI security has been one of the core topics in almost every risk management discussion. Because it covers both sides of that equation. It protects models, data, applications, APIs, and AI-powered workflows from attacks, while AI can also help detect threats, analyze network activity, and respond to suspicious behavior.
That’s why enterprises are prioritizing building security into AI systems from the start, which can help organizations scale AI without expanding their risk exposure. Such as strong access controls, data protection, adversarial testing, API security, continuous monitoring, and AI governance.
AI security refers to the technologies, processes, and governance controls used to protect artificial intelligence systems, the data they use, and the applications built around them. It also includes using AI to improve cybersecurity operations, such as detecting suspicious network activity or identifying unusual endpoint behavior.
Consider an enterprise software company deploying an AI assistant for its developers. The assistant may access source code, internal documentation, project tickets, APIs, and cloud environments. Protecting the model alone would not be enough. The organization also needs to secure the data entering the system, control who can access it, validate what the AI produces, and restrict what actions it can take.
That broader view is becoming increasingly important as AI moves deeper into enterprise workflows.
AI can process large volumes of network activity and identify patterns that may indicate a security threat. Instead of relying only on predefined rules, AI-powered security tools can learn from traffic patterns and flag unusual behavior.
Common applications include:
Endpoints such as laptops, servers, and employee devices generate enormous amounts of security data. AI can analyze this activity to identify suspicious behavior that conventional signature-based detection may miss.
Key applications include:
Suppose an employee’s laptop suddenly launches an unfamiliar process, accesses sensitive files, and attempts to communicate with an external server. AI-powered endpoint security can connect those events and flag the behavior as suspicious, allowing the security team to isolate the device before the incident spreads.
AI security and AI risk are closely related, but they address different questions.
AI risk is the broader discipline. It considers what could go wrong, how likely it is to happen, and what the consequences could be. AI security addresses the cybersecurity portion of that risk through controls that protect systems, data, models, and users.
As enterprises connect AI to more data and business processes, the attack surface becomes more complex. Some risks are familiar cybersecurity problems appearing in a new environment, while others are specific to AI applications.
Enterprise AI systems may process customer records, financial information, source code, contracts, employee data, or proprietary business information. Poor access controls or inappropriate data handling can expose information through prompts, model outputs, logs, or connected applications.
For instance, a customer-service copilot may have access to thousands of customer records. If permissions are not carried into the AI workflow, an employee could potentially receive information outside their authorized scope. Prompt injection occurs when malicious or manipulated input influences an AI system to behave in an unintended way. The risk becomes more significant when an AI application can retrieve internal information or use external tools.
Imagine an enterprise research assistant reading documents from both internal and external sources. A malicious document could contain hidden instructions designed to influence the assistant’s behavior. If the system blindly follows those instructions, the attack could move beyond the model and affect connected workflows.
Enterprise AI rarely operates in isolation. Applications connect models to databases, APIs, cloud services, business applications, and internal tools.
Each connection introduces another security boundary.
An AI agent that can create support tickets, update customer records, or trigger a workflow needs tightly controlled permissions. If its API access is broader than necessary, a compromised or manipulated request could produce consequences well beyond the original AI interaction.
AI systems depend on the quality and integrity of their data. If training, fine-tuning, retrieval, or embedding data is manipulated, attackers may influence system behavior.
This can be particularly difficult to detect because the system may continue operating normally while producing compromised results.
OWASP’s 2025 LLM guidance specifically identifies data and model poisoning as a security risk and also highlights vulnerabilities involving vector databases and embeddings used in RAG architectures.
The more actions an AI system can perform, the greater the consequences of a security failure.
An internal assistant that only summarizes documents has a limited blast radius. An AI agent that can send emails, approve transactions, modify records, or execute code has considerably more responsibility.
OWASP identifies excessive agency as a major LLM application risk, particularly as organizations give AI systems greater autonomy.
Security controls need to follow the AI system across its lifecycle rather than being added after deployment.
Start by understanding what information an AI system can access and why it needs that access. You can use:
The principle is straightforward: an AI system should receive the information required for its task, from sources it is authorized to use.
AI applications should be tested under conditions that resemble real attacks, not just normal user interactions.
Security teams can use prompt injection testing, adversarial testing, red teaming, input validation, and output validation to identify weaknesses before deployment.
Testing should also continue after launch because AI applications change as models, prompts, retrieval sources, integrations, and workflows evolve.
Every tool connected to an AI application should have clearly defined permissions.
Apply least-privilege access, strong authentication, rate limiting, input validation, and output validation. High-impact actions should also include human approval where appropriate.
For example, an AI procurement assistant may recommend a vendor automatically, but approving a large purchase could still require human authorization. Technology controls alone cannot determine whether an AI system should be deployed.
Organizations need policies covering data access, model selection, third-party AI services, monitoring, incident response, user permissions, and acceptable use.
NIST’s AI RMF describes trustworthy AI as including characteristics such as security, resilience, privacy, accountability, transparency, and reliability.
Governance provides the structure for translating those principles into operational decisions.
Security does not end when an AI application goes live. Organizations should monitor:
Continuous monitoring can reveal changes that were not visible during initial testing and provide evidence for investigating incidents.
Enterprises do not need to build their security approach from scratch. Several established resources can provide a foundation.
NIST AI Risk Management Framework (AI RMF)****: Provides a structured approach for governing, mapping, measuring, and managing AI risks throughout the AI lifecycle. NIST notes that the framework is voluntary and is currently being updated.
NIST Cyber AI Profile**:** NIST’s preliminary Cyber AI Profile focuses specifically on cybersecurity challenges associated with AI. Its three focus areas are **Securing AI System Components, AI-Enabled Cyber Defense, **and Thwarting AI-Enabled Cyber Attacks.
OWASP Top 10 for LLM Applications**:** Provides practical guidance around application-level risks such as prompt injection, sensitive information disclosure, supply-chain vulnerabilities, data and model poisoning, excessive agency, and vector or embedding weaknesses.
Together, these resources help security and technology teams approach AI from both the risk-management and application-security perspectives.
AI is getting embedded in enterprise applications, development environments, data platforms, and security operations. That creates significant opportunities, but it also changes where organizations need to place their security controls.
A secure AI strategy therefore needs to cover more than the model. It should account for the data going into the system, the applications around it, the APIs and tools it can access, the actions it can perform, and the people responsible for governing it.
For an enterprise deploying an AI assistant today, that could mean enforcing role-based access before connecting it to internal data, testing it against prompt injection before production, restricting the APIs available to an AI agent, and continuously monitoring its behavior after deployment. As AI systems become more capable and autonomous, AI security will become a core requirement for enterprise AI adoption, not an additional layer added after deployment.
AI Security 2026: Role, Risks, and Best Practices was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.