AI Security Best Practices: The Complete Guide

Artificial intelligence has moved from pilot project to core enterprise infrastructure faster than most security programs can adapt. AI is automating workflows, surfacing insights from complex datasets, and changing how work gets done across every function. But with that acceleration comes a new and expanding attack surface that most organizations are only beginning to understand.

AI security is not a subset of traditional cybersecurity. It is a distinct discipline that intersects data governance, model integrity, endpoint risk, and regulatory compliance in ways that legacy tools were never built to address. Organizations that treat AI security as an extension of existing controls will find those controls failing at exactly the wrong moments.

This guide covers what security leaders need to know: the risks unique to AI systems, the frameworks worth aligning to, the controls that actually matter, and the critical distinctions between securing generative AI and the newer, more complex challenge of agentic AI.

Why AI Introduces Unique Security Risks

Traditional IT security assumes a relatively stable set of actors, systems, and data flows. AI upends each of those assumptions. Models are trained on sensitive data, continuously updated, and deployed across cloud environments, endpoint devices, and third-party APIs. That architecture creates multiple points of exposure that do not map cleanly onto existing control frameworks.

The most consequential risk categories for AI systems include:

• Data vulnerabilities. Training data often contains sensitive or proprietary information. Insufficient protection creates paths to data leaks, model theft, and compliance violations. Vector embeddings and unstructured documents used in retrieval-augmented generation (RAG) systems are frequent blind spots.
• Model attacks. Adversarial inputs, data poisoning, and model inversion attacks can manipulate AI outputs in ways that are difficult to detect. A model producing subtly wrong answers at scale is a serious risk, especially in regulated industries.
• Operational exposure. AI deployed across cloud platforms, endpoint devices, and edge environments dramatically increases the attack surface. Shadow AI, unsanctioned tools adopted by employees without security review, compounds this exposure.
• Regulatory noncompliance. AI systems routinely process personally identifiable information (PII), financial data, and protected health information. Without governance controls, organizations face compounding compliance risk across GDPR, HIPAA, CCPA, and sector-specific frameworks.

What makes these risks especially hard to manage is that they often manifest through data. Securing AI is inseparable from securing the data these systems rely on. This is why data security posture management (DSPM) and data loss prevention (DLP) capabilities have become foundational to AI security programs, not optional layers added on top.

AI Security Frameworks Worth Aligning To

A growing set of standards provides structured guidance for AI security. Security teams should not try to build programs from scratch when these frameworks offer tested starting points.

NIST AI Risk Management Framework

The NIST AI RMF provides structured guidance for identifying, assessing, and managing AI risks across the full development and deployment lifecycle. It is organized around four core functions: Govern, Map, Measure, and Manage. Unlike prescriptive compliance checklists, the NIST framework is designed to be adaptable across industries and AI use cases, making it a practical foundation for organizations at different stages of AI maturity.

ISO/IEC 23894

ISO/IEC 23894 addresses AI system security with a focus on confidentiality, integrity, and availability. It provides organizations with a framework for embedding security considerations into AI system design, rather than treating security as an afterthought during deployment.

CSA AI Security Guidelines

The Cloud Security Alliance has published AI security guidance specifically oriented toward cloud-based AI deployments, covering topics from data governance to responsible AI deployment practices. For organizations running AI workloads in public cloud environments, CSA guidelines complement NIST and ISO standards with cloud-specific controls.

Aligning with these standards serves two purposes: it formalizes AI security policies into measurable controls, and it provides the audit trail that regulators and enterprise customers increasingly expect. Compliance also disciplines innovation, ensuring that AI projects are assessed for organizational risk before deployment rather than after.

Core AI Security Controls and Best Practices

Effective AI security requires a layered approach that spans people, processes, and technology. The following controls represent the foundation of any serious AI security program. And these security controls are rapidly becoming table stakes for organizations across industries.

According to the IBM 2025 Cost of a Data Breach Report, 97% of organizations that experienced an AI-related breach lacked proper AI access controls, and 63% of breached organizations either have no AI governance policy or are still developing one. It’s clear that enterprises have work to do to harden the AI attack surface.

Data Security and Governance

Protecting the data that feeds AI models is the most foundational control. AI systems ingest training data, generate new data through inference, and often cache or store interaction history in ways that security teams have limited visibility into. DSPM solutions extend traditional data security to AI workloads, providing visibility into training datasets, unstructured documents, and vector embeddings.

Effective data governance for AI means knowing what data is being used to train or prompt AI systems, classifying that data accurately, and enforcing policies that prevent sensitive information from flowing to unauthorized models or third-party services. Without this foundation, every other AI security control operates with incomplete information.

Model Integrity and Access Management

AI models represent significant investments and, in many cases, encode proprietary data and decision logic. Controlling who can access models, how they can be queried, and what inputs they receive is essential for preventing unauthorized use and adversarial manipulation.

Role-based access controls, strong authentication, and behavioral monitoring of model usage help detect anomalies before they become incidents. Organizations should also consider model versioning and lineage tracking, which enable auditability and rapid response when a model produces unexpected outputs.

Secure Development Lifecycle for AI

Security cannot be retrofitted onto AI systems after deployment. Threat modeling during model design, adversarial testing before release, and continuous monitoring post-deployment are all essential. Shadow AI projects and unmanaged data repositories are among the most common sources of unmitigated risk and often the last places security teams look.

Monitoring and Incident Response

Continuous monitoring of AI systems creates the baseline visibility needed to detect anomalies before they escalate. Incident response protocols must be updated to specifically address AI-related events, including model tampering, data exfiltration through AI interfaces, and unusual inference patterns that may signal adversarial inputs. A response plan built for traditional IT breaches will not adequately cover AI-specific incidents.

Device and Infrastructure Hardening

AI workloads run on cloud platforms, on-premises servers, and increasingly on endpoint devices. Each environment requires hardening with secure configuration baselines, network segmentation, encryption at rest and in transit, and regular patching. Endpoint AI agents, discussed in detail in the agentic AI section below, deserve particular attention because they operate with filesystem access and process-level permissions that traditional DLP tools were not designed to monitor.

Security Differences: Generative AI vs. Agentic AI

The security considerations for generative AI and agentic AI are related but fundamentally different in scope, attack surface, and required controls. Most organizations are still primarily focused on generative AI risks, even as agentic AI is already operating inside their environments.

Generative AI Security

Generative AI, including tools like ChatGPT, Gemini, Microsoft Copilot, and enterprise LLMs, primarily creates security risk through the data that flows into and out of the model. A Gartner survey conducted between May and November 2025 found that over 57% of employees use personal GenAI accounts for work purposes, and 33% admit to inputting sensitive information into unapproved tools. The interaction is typically initiated by a human user, mediated through a browser or application interface, and bounded by a conversational session.

The core security risks for generative AI are:

• Sensitive data entered into prompts. Employees routinely paste proprietary source code, customer PII, financial forecasts, and internal strategy documents into AI chat interfaces without understanding the data handling implications. Cyberhaven's research found that 39.7% of AI interactions involve sensitive data.
• Confidential data surfaced in AI outputs. AI tools can regenerate or surface confidential content to unauthorized users, creating IP exposure and compliance risk that is difficult to detect after the fact.
• Shadow AI adoption. Security teams lack visibility into the full range of AI tools employees are using. The most AI-intensive enterprise environments are utilizing over 300 generative AI tools. Most were never reviewed or sanctioned.

For generative AI, the security program needs to: discover which tools are in use, assess the risk profile of each tool, monitor what data is flowing to and from those tools, and enforce policies that align AI usage with organizational risk tolerance. This is achievable with the right combination of shadow AI discovery, data lineage, and DLP controls.

Agentic AI Security: A Different Control Plane

Agentic AI represents a more significant and less understood security challenge. Unlike generative AI tools that respond to human prompts through a browser interface, agentic AI systems operate autonomously on endpoint devices, executing tasks, reading files, calling APIs, and storing interaction history without requiring a human in the loop for each action.

Tools like Claude Code, Codex, and open-source agents are already operating on enterprise endpoints with the kind of access previously reserved for human employees: reading clipboard data, spawning processes, accessing the filesystem, and calling external APIs. Research from Cyberhaven Labs shows that roughly half of all developers were using desktop-based coding assistants by late 2025, up from about 20% at the start of that year, and nearly 23% of enterprises had adopted agent-building platforms by early 2026.

The security differences are significant:

• Human speed vs. machine speed. Generative AI interactions happen at human speed, giving security teams some opportunity to detect anomalies in time to intervene. Agentic AI operates continuously in the background, running at machine speed with no natural pause for review.
• Browser-bounded vs. OS-level access. Generative AI risks can often be addressed through browser-level controls and network traffic inspection. Endpoint AI agents operate at the OS level, using accessibility APIs and local process calls that bypass network-based DLP entirely.
• Session-based vs. persistent context. Generative AI interactions are typically bounded by a session. Agentic systems maintain a persistent context window stored locally, essentially a searchable index of every file, interaction, and data fragment they have encountered. That history does not expire.
• Known applications vs. unmanaged agents.Traditional DLP is designed around a known set of applications and network destinations. Agentic AI introduces new local processes and background agents that fall outside the application inventory security teams have built their policies around.
• Human cultural norms vs. none. Human employees, however imperfectly, carry some internalized sense of what data should and should not be shared. Agents operate without those norms. They will use production PII to test a model if that data is available and no control prevents it.

The practical implication for security teams is that the control strategies built for generative AI will not scale to agentic AI. Blocking agents at the network level does not work because many agents require no internet connection. Reviewing each new tool manually is not feasible given the pace at which new agent frameworks are emerging. The only durable answer is to shift focus from controlling the tools to protecting the data itself.

This requires endpoint visibility that goes beyond what traditional DLP can provide: the ability to observe what data an agent is accessing, trace that data's origin and transformations, and enforce controls based on context rather than application names or network destinations.

Legacy security tools assume data moves through networks you control, humans are the primary actors, and applications are known and relatively static. Agentic AI breaks every one of those assumptions.

AI Security Best Practices: An Operational Checklist

For CISOs and security leaders building or maturing an AI security program, the following practices represent the highest-leverage controls across both generative and agentic AI:

• ☐ Deploy DSPM for AI to discover, classify, and monitor sensitive information used by models, including training datasets, vector embeddings, and RAG document stores.
• ☐ Encrypt datasets at rest and in transit across all AI workloads, including intermediate data stores generated during inference and fine-tuning.
• ☐ Implement shadow AI discovery to build a registry of all AI tools in use across the organization, including unsanctioned tools and tools embedded in existing SaaS applications.
• ☐ Apply role-based access controls and strong authentication across AI platforms, APIs, and endpoint agents.
• ☐ Conduct red-teaming exercises and adversarial testing focused on AI workloads, not just traditional application security.
• ☐ Monitor AI outputs and data access patterns continuously, with alerting calibrated to detect anomalous behavior rather than generate noise.
• ☐ Establish endpoint-level visibility for agentic AI, specifically the ability to observe which agents are running, what data they are accessing, and whether that access aligns with policy.
• ☐ Maintain audit trails and model lineage documentation to support explainability, incident investigation, and regulatory compliance.

AI security is an evolving discipline, and the pace of AI adoption means the threat landscape is changing faster than most security programs can track. The organizations that will navigate this well are not the ones trying to block AI, because that approach consistently fails. They are the ones building security programs centered on the data AI touches, rather than on the AI tools themselves.

The goal of security has never been to stop work from happening. It is to allow data to move freely within legitimate business workflows while preventing it from escaping those boundaries. For generative AI, that means visibility into what data is flowing to which tools and enforcement that aligns AI usage with organizational risk tolerance. For agentic AI, it means going deeper: endpoint-level observability, data lineage tracking, and controls that can distinguish between an agent doing legitimate work and one operating outside the boundaries your security program is designed to protect.

Every enterprise will adopt AI agents. The only question is whether security teams will have the visibility and control to govern that adoption when it happens, or whether they will discover the gaps only after sensitive data has already left the environment.

Explore how enterprises are adopting and utilizing AI, and the new risk surface these tools create, with the Cyberhaven 2026 AI Adoption & Risk Report.

Frequently Asked Questions

What are AI security best practices?

AI security best practices combine technical, procedural, and governance measures to protect AI systems, models, and the sensitive data they rely on. They include securing training data, monitoring model behavior, implementing access controls, discovering shadow AI usage, and aligning with recognized frameworks like NIST AI RMF and ISO/IEC 23894. For agentic AI specifically, best practices extend to endpoint visibility and data lineage tracking.

How do generative AI and agentic AI security differ?

Generative AI security focuses primarily on controlling what data flows into and out of AI tools through user interactions, typically manageable with browser-level controls, DLP, and shadow AI discovery. Agentic AI security is more complex: agents operate autonomously at the OS level, run at machine speed, maintain persistent local context, and access data in ways that bypass traditional network controls entirely. Agentic AI requires endpoint-level visibility and data-centric controls that can observe and govern agent behavior directly.

What are the most important AI security controls?

The highest-priority controls are data governance and DSPM for AI, shadow AI discovery, model access management, endpoint visibility for agentic AI, and continuous monitoring with incident response protocols tailored to AI-specific events. Organizations that treat AI security as an extension of their existing DLP program, without expanding to endpoint and data lineage capabilities, will have significant blind spots.

What AI security frameworks should my organization follow?

The NIST AI Risk Management Framework, ISO/IEC 23894, and CSA AI Security Guidelines provide structured, adaptable foundations for AI security programs. They support risk identification, measurable controls, and audit readiness without requiring a specific technology stack.

Why is data security central to AI security?

AI systems are only as secure as the data they process. Compromised training data leads to model poisoning. Uncontrolled data flows lead to leaks and compliance violations. Inadequate data visibility means security teams cannot detect when AI tools are accessing information they should not. DSPM for AI provides the visibility, governance, and enforcement needed to protect data across all AI workflows, making it foundational rather than supplementary to AI security programs.