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Organizations around the world are increasingly developing or deploying AI-powered tools to streamline operations and scale efficiently. However, the benefits come with the inherent and unpredictable risks of AI, which must be mitigated with appropriate safeguards.
One of the biggest challenges to AI security is the lack of formal oversight. According to Vanta's State of Trust Report, only 36% of organizations have implemented or are building AI-informed security policies. This is an alarming gap because without robust policies and procedures, teams cannot ensure safe and scalable adoption of AI.
In this article, Vanta explores eight fundamental AI security practices that teams should implement to minimize risk exposure and strengthen governance.
Description of scope to protect AI
AI security involves implementing policies, procedures, and controls that protect AI tools from threats such as attacks, unauthorized access, and manipulation. The scope is likely to be greater than traditional cybersecurity, as organizations now increasingly rely on AI to drive core workflows and business decisions, and security disruptions can be more damaging than ever.
According to the 2025 Stanford AI Index Report, AI-related incidents in business have increased by more than 56% over the past year. Today, a single vulnerability, such as a data breach or algorithm error, can cause massive and unpredictable disruption.
Remember that the scope of protecting AI spans its entire lifecycle. Protection measures must be defined and integrated during early planning and design, from training, deployment, and ultimately decommissioning.
Core AI risks that drive security best practices
AI systems face the same threats as traditional systems and networks, as well as new vulnerabilities and attack vectors that are unique to their design, behavior, and use cases.
“Introducing AI into an organization introduces a variety of new and often complex security risks, so understanding your unique AI threat landscape is essential to properly protecting your AI systems. To address this, perform threat modeling early in the AI development lifecycle to ensure that AI-specific risks are reasonably mitigated.” – Ethan Heller
GRC Subject Matter Expert, Vanta
The four most impactful AI risk triggers are:
1. Data breach
AI systems are designed to process large amounts of data. Every access point is a potential vulnerability. Incidents can occur due to weak access controls, insecure APIs, or adversarial attacks targeting the model's data flow.
In addition to immediate loss of data, breaches can result in regulatory scrutiny (e.g., HIPAA, GDPR, SOC 2), which can result in severe fines, business interruption, and damage to reputation and trust.
relief: Implement standard defenses such as strict data protection, role-based access, and robust encryption to keep sensitive information safe in transit and at rest.
2. Information bias and discrimination
AI tools rely on training data to generate responses for target use cases. If the data contains bias, AI will amplify the bias over time, skewing results and creating discriminatory patterns in decision-making. Prejudicial outcomes can be particularly detrimental in industries such as health care and insurance, which are subject to strict anti-discrimination laws.
relief: You can manage bias by regularly auditing your training data to ensure it's relevant, unbiased, and factually correct. Consider reweighting and adversarial approaches to reduce bias in the dataset.
3. Manipulating training data
Training data manipulation occurs when an individual changes the data used to train an AI model through unintentional corruption or malicious acts. If changes to AI data are not accounted for, they can negatively impact the reliability, safety, and accuracy of AI output.
relief: Establish strict safeguards and monitoring protocols for training data. Human validation steps can also be implemented both before and during training to identify undocumented changes.
4. Resource exhaustion
Resource exhaustion is caused by malicious attacks such as DDoS that aim to overload AI systems, reducing performance and causing operational disruption. Depending on the nature of the use of AI, this could lead to customer dissatisfaction and contractual penalties.
relief: Implement safety measures such as load balancing, rate limiting, and resource isolation. Mature organizations may also deploy automated monitoring to detect such attacks early.
8 AI security best practices to follow
From an AI governance perspective, here are some scalable security best practices you can adopt.
1. Establish data security policies across the AI lifecycle
Protecting data integrity is one of the fundamental principles of AI security management. AI tools access large amounts of data, and any modification, loss, or unauthorized access can reduce model accuracy and erode trust.
Security teams must view data security as an ongoing responsibility rather than a one-time management. This means classifying and labeling sensitive data from the beginning, allowing stage-specific rules to be implemented during data collection, training, and refinement.
We recommend documenting the data integrity validation measures your team is required to implement, such as encryption in transit and at rest, anomaly detection, and adversarial testing. Incorporate these into your operational policies to ensure that validation practices are not deprioritized at any point in the AI lifecycle.
In addition, a disposal protocol for obsolete datasets must also be defined. To prevent unauthorized use, have a senior executive, such as a CISO, review disposal.
2. Track version history using digital signatures
An effective way to verify changes to AI systems is to use authentication tools such as digital signatures. These allow you to track dataset and configuration updates during model training, tuning, tuning, or reinforcement learning.
To implement this, you need to use cryptographic signatures on the original version of the data and have the parties making the changes time-stamped and signed, adding visibility and accountability. This exercise provides an overview of custody processes that can be useful during security or compliance investigations.
3. Adopt zero trust principles
Because AI is unpredictable, we follow Zero Trust principles for all systems and workloads supported by AI. This approach leverages segmented controls to reduce the attack surface and limit rogue insider threats.
In the context of AI security, zero trust means never assuming implicit trust and establishing controls that validate all users, processes, and devices before being granted access to AI tools.
You should also adopt a Zero Trust model in your physical environment. Isolate your AI assets in a secure location and implement safeguards for access control, such as monitoring, multi-factor authentication, and keycard systems.
4. Thorough access control
Access control helps operationalize Zero Trust principles. Role-based access control (RBAC) is an efficient way to scale execution, ensuring that stakeholders only interact with the AI models, datasets, and tools needed for their role, minimizing the risk of accidental disclosure or misuse.
A best practice is to combine RBAC with the principle of least privilege on your data. This means giving users and AI models access to the minimum amount of information needed for a given task. It also helps to have a clear hierarchy of who can access, modify, export and share AI resources.
5. Dispose of your data securely
When decommissioning an AI system, the risk of model or data duplication is very high. This can be alleviated by following strict disposal procedures. Some of the standard methodologies described in NIST Special Publication 800-88, Guidelines for Media Sanitization, are available.
Here's a summary of the recommended methods:
- Clear: Apply logical techniques to sanitize sensitive information and training data, such as overwriting the storage location using read or write commands or factory resetting the device if rewriting is not supported. This method protects against non-invasive recovery methods, but may not withstand advanced threats.
- purge: More comprehensive methods such as overwriting, block erasure, and cryptographic erasure are used to make data unrecoverable even in advanced ways.
- Destroy: Use physical methods such as shredding paper documents or degaussing electronic media to make the media unusable, making data recovery impossible.
6. Conduct risk assessments frequently
The rapid pace of AI evolution requires frequent re-evaluation of systems to identify new vulnerabilities. When it comes to AI risk assessment, it is common for organizations to take a risk-based approach. These can be run regularly at a defined frequency or whenever a change is made that impacts how your organization uses AI.
Regular assessments are also essential for early detection of issues such as AI drift. Over time, training data can become irrelevant or no longer match your intended AI use case. Leaving this unchecked can result in inaccurate responses and may violate regulations and contracts.
To stay current with industry best practices, align your risk assessment procedures with standard AI frameworks such as NIST AI RMF and ISO 42001.
7. Establish an incident response plan
Even with comprehensive AI security measures in place, incidents can occur, so you should create an AI-aware incident response plan (IRP) to inform your next steps. This document details AI-specific adverse events that organizations may encounter and strategies to minimize their operational impact.
To be effective, the IRP must include detailed information about:
- Steps to identify, respond to, and mitigate risks
- Stakeholder roles and responsibilities
- communication protocol
- Recovery strategies for dealing with AI systems
Although you can use templates to structure your IRP, it's best to treat it as a living document. Review and update regularly to keep pace with changes in AI threats and the regulatory environment. Run simulations regularly to ensure that procedures are handling the pressures and that stakeholders are ready to respond quickly.
8. AI System Monitoring and Logging
Continuous monitoring drives a comprehensive AI security program and is a core requirement of many AI frameworks and regulations. Continuous monitoring helps uncover anomalies, unauthorized access, and shadow AI before they escalate into broader threats.
First, log all AI interactions, updates, and access events. While these support incident detection and auditing, continuously tracking the vast amount of system activity in an AI environment can quickly become overwhelming.
Manual monitoring workflows put a lot of pressure on security teams and increase the risk of inefficiencies, missed controls, and delays. Many organizations address these challenges by adopting popular GRC tools that centralize logging, risk tracking, and policy monitoring across their security programs. As part of this broader GRC ecosystem, a purpose-built AI compliance solution can automate repetitive processes and free up your team to focus on other strategic tasks.
this story produced by Vantaa Reviewed and distributed by stacker.
