What Is AI Security?

In essence, AI security merges traditional cybersecurity principles with the unique demands of artificial intelligence solutions. Because AI systems learn from extensive training data, they are susceptible to tampering if that data is compromised or maliciously altered. Protecting AI use extends beyond algorithms and models to ensure the reliability, confidentiality, and integrity of all associated datasets. When combined with robust data security measures, these efforts contribute to a truly resilient AI environment.

Generative AI, including applications like ChatGPT, elevates machine learning’s potential but also paves the way for new exploits. If these models are manipulated, security teams risk not only encountering a sudden data breach but also facing lasting repercussions for intellectual property. As AI continues to advance, businesses operating in diverse sectors must invest in comprehensive defenses to guard against these challenges.

As organizations accelerate AI initiatives, four security pressure points are emerging that can quickly undermine data integrity, confidentiality, and trust:

• GenAI adoption risks: Rapid deployment can outpace security controls, expanding the attack surface across prompts, plugins, integrations, and model workflows.
• Shadow AI: Employees may use unapproved AI tools for speed and convenience, bypassing IT oversight and creating unmanaged data and compliance exposure.
• Data leakage via LLMs: Sensitive information can be inadvertently shared through prompts, training inputs, logs, or model outputs, risking IP loss and regulatory violations.
• AI governance challenges: Without clear policies, ownership, and guardrails, it becomes difficult to enforce consistent standards for data handling, model usage, auditing, and accountability.

AI security works by applying layered controls across the full AI pipeline—not just at the perimeter—so risks are addressed where they originate and where they manifest. This end-to-end approach improves resilience, supports AI security best practices, and reduces operational surprises during scale-up.

• Data collection: Classify and minimize data, enforce consent and retention rules, and protect sources to strengthen AI data security from the first mile.
• Model training: Validate datasets, secure training infrastructure, and test for abuse cases (e.g., leakage, harmful behaviors) to reinforce AI model security.
• Deployment: Harden runtime environments, isolate workloads, control tool/plugin access, and implement safe defaults for production-grade enterprise AI security.
• User interaction: Apply prompt and content controls, redact sensitive inputs/outputs, and prevent unsafe tool execution to support safer generative AI security.
• Monitoring: Continuously detect anomalies, abuse patterns, drift, and data leakage indicators—then respond with playbooks and corrective actions.

Maintaining a solid AI security posture typically revolves around several core pillars. These components aim to establish a framework that incorporates data protection, technical safeguards, and organizational readiness:

• Data protection: Ensures that all training data remains accurate and shielded from exposure.
• Continuous monitoring: Enables proactive risk management to recognize and respond to vulnerabilities in real time.
• AI security posture management: Maintains AI security practices and readiness through ongoing assessment and improvement.
• Risk assessment: Identifies and prioritizes AI-specific threats to guide targeted risk mitigation efforts.
• Incident response: Establishing procedures for detecting, responding to, and recovering from security breaches and incidents involving AI applications.
• AI governance and compliance: Adhering to relevant laws and regulations like the GDPR, CCPA, and AI Act, which are essential for navigating the complex landscape of AI applications.

AI security extends traditional cybersecurity by protecting not only systems and networks, but also model behavior, training data, and AI-driven decision pathways. In practice, organizations that prioritize AI security gain stronger governance, faster detection of AI-specific threats, and better alignment with modern secure AI adoption goals.

AI security risks span far beyond standard malware and network intrusion—they also target how models behave, what data they learn from, and how people interact with AI-enabled workflows. For enterprise AI security teams, understanding these risk categories is a practical starting point for secure AI adoption and consistent AI governance and security.

AI Model Risks

AI model security focuses on threats that alter, exploit, or manipulate model behavior—especially in generative AI security, where inputs and outputs can be weaponized at scale. These risks can degrade reliability, expose sensitive information, or create downstream business harm even when traditional controls appear “green.”

• Model poisoning: Attackers manipulate training data or fine-tuning inputs so the model learns harmful patterns, hidden triggers, or biased behaviors.
• Prompt injection: Malicious instructions are embedded in prompts or external content (e.g., documents, web pages, tools) to override system rules and coerce unsafe actions or disclosures.
• Adversarial attacks: Carefully crafted inputs are designed to cause incorrect classifications or unsafe outputs, exploiting model weaknesses rather than software vulnerabilities.

Data Risks

AI data security addresses how sensitive information can be exposed through collection, storage, training pipelines, logs, and outputs. Because AI systems often aggregate and transform large datasets, a single weak point can create disproportionate confidentiality and compliance impact.

• Training data leakage: Proprietary or personal data may be recoverable through model outputs, inference techniques, or insecure storage and sharing of training artifacts.
• Sensitive data exposure: Users or systems may inadvertently submit confidential data via prompts, uploads, telemetry, or integrations—creating leakage and regulatory risk.

User Risks

Users are often the fastest path to compromise, and AI changes the scale and credibility of social engineering. In enterprise AI security, user-facing risks frequently blend technical exploitation with trust manipulation.

• AI phishing: AI-generated messages can be highly targeted, polished, and context-aware, increasing click-through rates and credential theft success.
• Deepfake attacks: Synthetic audio/video can convincingly impersonate executives or vendors, enabling fraud, account takeover, or payment redirection schemes.

An AI security framework aligns people, process, and technology so organizations can manage AI security risks consistently across teams and vendors. It also provides a structured way to operationalize AI governance and security—from policy and design to deployment and continuous monitoring.

• NIST AI RMF: A risk management approach that helps organizations map, measure, and manage AI risks across the AI lifecycle with governance and accountability.
• Zero trust for AI: Extends zero trust principles to AI systems by continuously verifying identity, device, data access, and tool/model permissions—never assuming prompts, plugins, or integrations are safe by default.
• Secure AI lifecycle: Embeds security controls into each stage (design, data handling, training, evaluation, deployment, and retirement) to reduce systemic risk rather than relying on after-the-fact fixes.
• Model governance: Establishes ownership, approval workflows, auditing, and documentation so models are used as intended, changed safely, and monitored for drift and misuse.

As AI weaves into everyday operations, businesses operating under strict data protection regulations must address growing demands around privacy, fairness, and compliance. Several factors emerge when exploring regulation, ethics, and technology.

Data Protection Laws

Compliance with data protection regulations, including the California Consumer Privacy Act (CCPA), defines how AI solutions collect and handle personal information. Companies must disclose their data practices and safeguard user data to avoid legal liabilities. In many regions, consumers also have the right to understand and control how their data is utilized.

Ethical Transparency

As artificial intelligence security grows more pivotal, transparency fosters trust by revealing some of the logic behind AI-driven decisions. Clear documentation of algorithms and training processes promotes accountability and helps minimize biases. This approach also supports security teams in better understanding the factors influencing system behavior.

Protecting Intellectual Property

Organizations face significant challenges keeping proprietary models secure, especially in large language model (LLM) deployments. Ensuring the confidentiality of specialized code, unique model architectures, and advanced solutions preserves a competitive edge. Strong protective practices reduce the likelihood of leaks or thefts that could harm a company’s reputation.

Enterprise AI security succeeds when controls are practical enough for daily use and strict enough to meaningfully reduce AI security risks. The following AI security best practices are designed to strengthen AI data security and AI model security while enabling innovation without uncontrolled exposure.

• Secure GenAI usage: Define approved tools and use cases, require safe prompting patterns, and restrict risky features (e.g., unrestricted web/tool access) to support secure generative AI security at scale.
• Implement AI access control: Apply least privilege to models, datasets, and tools; enforce strong authentication and role-based policies for who can use which models and capabilities.
• Prevent AI data loss: Classify and redact sensitive data, restrict what can be pasted/uploaded, and apply DLP-like controls tailored to prompts, logs, and model outputs for stronger AI data security.
• Inspect AI traffic: Monitor and analyze model/API calls, prompt/response telemetry, and plugin/tool execution to detect prompt injection, exfiltration attempts, and policy violations.
• Enforce AI governance: Establish ownership, documentation, evaluation standards, audit trails, and ongoing reviews so AI governance and security remain consistent across teams and vendors.

Zscaler enables AI security by extending zero trust principles across the full AI pipeline—so organizations can discover AI use (including shadow AI), govern access, and protect sensitive data where it’s most likely to leak: in prompts, responses, and AI-connected workflows. It pairs inline controls for generative AI and AI SaaS usage with continuous, automated testing and runtime guardrails, reducing the gap between “AI innovation” and “AI-ready security” as deployments scale. 

And, because governance is now a moving target, Zscaler helps operationalize AI governance and compliance by mapping discovered risks to evolving frameworks and policies so teams can prioritize remediation and stay audit-ready:

• Control AI access at the user level by discovering AI app usage and applying flexible policies to allow, block, warn/coach, or isolate interactions across popular GenAI apps, embedded AI in SaaS, and developer tooling.
• Prevent data leakage through prompts and responses with inline inspection and DLP that can detect and block sensitive content (e.g., source code, PII/PHI/PCI) while supporting acceptable-use guardrails and safer day-to-day GenAI productivity.
• Continuously test AI systems before and after launch using automated AI red teaming with prebuilt and customizable probes, multi-modal testing (text/voice/images/documents), and workflows to track and remediate issues in tools like Jira and ServiceNow.
• Simplify AI governance and compliance by automatically mapping AI risks to major frameworks (e.g., EU AI Act, NIST AI RMF, OWASP LLM Top 10, MITRE ATLAS, ISO/IEC 42001) and supporting custom policy creation/import to align AI deployments with internal standards.