What is AI Security?

AI security refers to the strategies, tools, and practices that safeguard artificial intelligence systems, models, and data from cyber threats, manipulation, and misuse. As organizations integrate AI into their critical operations, they need to protect those systems in order to maintain data integrity, comply with data protection and privacy regulations, and maintain customer trust.

This article explores what AI security entails and how it protects AI and data systems, and outlines some best practices for building secure, resilient AI infrastructures.

AI Security Explained

Enterprises are deploying AI across more and more workflows and the attack surface for hackers targeting AI is expanding. These threat actors can exploit vulnerabilities in training data, model logic, or connected systems to produce harmful outcomes or extract sensitive information. AI security is the protection of artificial intelligence systems—a category that includes machine learning models, agents, algorithms, and data pipelines—from unauthorized access, data breaches, adversarial attacks, and model manipulation.

AI has a dual role in modern cybersecurity: it's both a target that must be protected and a defender that aids security teams. Attackers may try to corrupt or exfiltrate model data, while defenders can use AI itself to detect and respond to those threats. For example, AI-powered anomaly detection can spot signs of ransomware activity in backup environments far earlier than traditional systems can, allowing security teams to isolate affected assets and begin recovery before damage spreads.

Effective AI and data security depends on having continuous visibility across all systems that store, process, or analyze AI data. That’s where data security posture management comes in: security teams (and their AI helpers) must identify sensitive information, monitor data access, and flag risks before they escalate.

Why AI and Data Security Matter More Than Ever

Organizations are deploying AI chatbots and assistants across customer service, software development, finance, and security operations—and every new model, agent, and data pipeline is a potential vulnerability. Exposed APIs can leak credentials or tokens.Iinsecure model training environments invite data poisoning. Over-permissive agents create opportunities for privilege escalation and lateral movement. The OWASP Top 10 for Large Language Model Applications highlights threats that arise from how companies build and integrate their AI systems, such as prompt injection, training-data poisoning, supply-chain vulnerabilities, and model denial-of-service attacks.

Recent studies confirm the urgency. The IBM 2025 Cost of a Data Breach Report pegs the global average breach cost at USD 4.44 million and highlights governance gaps around AI adoption. And unsecured AI systems threaten more than just data confidentiality. When models that drive automated decisions are manipulated—or when training data contains poisoned records—those models will produce erratic or incorrect results in ways that will be difficult to trace.

Companies must treat AI systems like high-value assets: instrument them for visibility, restrict access by default, and monitor for drift and abuse.

Key Components of AI Security

AI security covers multiple layers, which range from protecting individual AI models to governing the systems and data that support them. Each area has its own attack vectors and defense strategies:

Model security involves protecting AI models from theft, tampering, and adversarial inputs that manipulate model behavior. Attackers may attempt to reverse-engineer models or inject malicious data that changes outputs. Organizations need to secure AI models through encryption, model-integrity monitoring, and regular robustness testing.
Data security focuses on maintaining the confidentiality and integrity of training and inference data via encryption, strict access controls, and continuous data validation. Strong data protection practices also support compliance with privacy regulations and help prevent breaches of sensitive information.
Agent security addresses risks to autonomous and AI-powered agents that act within enterprise systems. These agents can be vulnerable to memory poisoning, privilege escalation, or logic manipulation. To secure AI agents, you must monitor their runtime behavior, enforce least-privilege permissions, and implement the capacity to roll back unexpected, unwanted, or malicious agent activity.
Infrastructure security protects the broader ecosystem—pipelines, APIs, and compute environments—used to train, deploy, and operate AI technologies. Common risks to infrastructure arise from cloud misconfiguration, unpatched dependencies, and exposed endpoints. Applying Zero Trust access policies and automated configuration management reduces those vulnerabilities.
Governance and compliance align AI use with frameworks such as GDPR, the NIST AI Risk Management Framework, and ISO 42001. Clear oversight and auditable controls help organizations manage model lifecycle risks and maintain regulatory accountability.

Effective programs for securing AI integrate these components into a unified platform, such as Rubrik Security Cloud, which delivers data protection, threat monitoring, and recovery orchestration across hybrid environments

AI Security Area	Common Threat	Example Defense
Training data	Poisoning attack	Data validation, anomaly detection
Model	Adversarial input	Model robustness testing
APIs	Unauthorized access	Token-based authentication
Infrastructure	Cloud misconfiguration	Zero Trust access policies

Table 1. Typical AI security threats and their defenses

Best Practices for Securing AI Systems

Securing AI systems requires continuous controls that protect data, models, and infrastructure throughout the AI lifecycle. The following best practices help reduce risk and strengthen resilience:

Apply Zero Trust principles to model and data access. Treat every interaction—with a human or machine—as untrusted until verified. Granular authentication, authorization, and segmentation can prevent attackers from exploiting lateral movement or over-permissive access.
Encrypt all sensitive datasets and model artifacts. Encryption protects both training and inference data, reducing the risk of data leakage or theft if an environment is compromised. Use strong key management and rotate keys regularly to maintain data confidentiality.
Continuously monitor AI model and agent behavior for drift and anomalies. Tracking deviations in output, performance, or decision logic can reveal early signs of poisoning or tampering. AI-driven observability tools can surface irregularities in real time, giving security teams faster insight into emerging threats.
Leverage AI-powered security analytics for real-time threat detection. Machine learning and anomaly detection can help identify ransomware activity, data exfiltration, or privilege abuse faster than traditional rule-based monitoring.
Test models and agents with red-teaming and adversarial simulations. Regular stress testing exposes vulnerabilities in model logic, training data, and access controls before attackers find them.
Govern training data sources to prevent bias, poisoning, or data leaks. Establish a clear chain of custody for datasets and verify integrity through hashing and validation.
Develop remediation plans for when AI models or agents execute unintended or adverse actions. Incident playbooks should include isolation procedures, rollback steps, and methods for restoring safe model states.

Many of these best practices align with Rubrik’s approach to securing AI environments. Rubrik integrates data protection, threat analytics, and cyber recovery to help organizations maintain control across every phase of the AI lifecycle.

How AI Enhances Security Operations

AI and machine learning have become critical allies in defending modern enterprises. By processing vast amounts of data faster and more accurately than human analysts ever could, AI technologies help organizations detect, predict, and respond to threats before they cause serious harm. Some AI-based security functions include:

AI-driven threat detection identifies anomalies in backup and production data that may indicate ransomware, exfiltration, or privilege abuse. Machine learning models analyze historical patterns to flag unusual encryption rates, file modifications, or data access spikes that traditional tools might overlook.
Predictive analytics and behavioral modeling allow security teams to anticipate attacks rather than react to them. By learning normal user and system behavior, AI-powered platforms can recognize deviations that suggest insider threats or early signs of ransomware activity.
AI in data protection and recovery has evolved beyond passive monitoring. For example, Rubrik’s Ruby generative AI companion extends Rubrik Security Cloud to help detect anomalies, assess threats, and guide recovery workflows. Built on Microsoft Azure OpenAI, Ruby operates within Rubrik’s secure environment—customer data never leaves the platform or trains external models.

One important thing to keep in mind: any AI security tools must be transparent and ethically deployed. Security teams must document how AI decisions are made, protect training data from bias or contamination, and maintain human oversight. Adhering to responsible AI principles helps organizations stay compliant with evolving privacy and cybersecurity regulations while strengthening their overall security posture.

What’s Next?

If enterprises want to be resilient, they must now secure their AI tools. As AI transforms how businesses operate, organizations need to safeguard their models, data, and infrastructure with intelligent, Zero Trust–aligned controls that anticipate threats and protect sensitive information at every layer.

Modern security demands more than reactive defense; it requires continuous protection and recovery across all AI systems. Rubrik’s AI-powered security solutions bring data protection, threat detection, and cyber recovery together in a single platform, helping organizations stay resilient in the face of evolving risks. Contact Rubrik to learn more or connect with an expert.