What is AI Zero Trust Security?

The zero trust security model combines least privilege access controls with explicit verification of user requests for access to corporate assets. AI-based zero trust security extends this by using AI to make access decisions and perform analytics. AI zero trust security is an evolution of Zero Trust Network Access (ZTNA), taking advantage of the technology to enhance identity-centric security. Bringing AI into the mix enables intelligent enforcement of least privilege access controls, continuous verification, and microsegmentation.

AI is increasingly integrated into zero trust architectures to address the growing number of identity-centric attacks that organizations face. As cyberattacks grow more numerous and sophisticated and increasingly focus on compromised user accounts, organizations need AI to implement effective security at scale, especially with the growth of remote workers and devices.

How Is Zero Trust Security Defined Today?

The zero trust security model was designed to address the limitations of traditional, perimeter-based security. The perimeter model, commonly implemented via virtual private networks (VPNs), is vulnerable to insider threats and lateral movement of attackers within the network. ZTNA and zero trust offer a modern alternative designed to fit the needs of evolving IT infrastructure and cyber threats.

Core Principles of Zero Trust

Zero Trust takes an “assume breach” approach to security, attempting to prevent lateral movement and ongoing attacks as well as incoming threats. Key elements of the model include:

• Least Privilege: The principle of least privilege mandates that users, devices, and applications should only have the access needed to perform their role. Least privilege access controls help to manage the potential impacts of a compromised account, managing the threat of lateral movement.
• Continuous Authentication and Authorization: Zero Trust security controls require explicit verification of each request for access to corporate assets. “Never trust, always verify.”
• Microsegmentation: ZTNA solutions implement microsegmentation, creating trust boundaries around individual systems and applications. This helps to enforce least privilege access controls since all requests must pass through this trust boundary and undergo inspection and policy enforcement.

Zero Trust controls apply equally to everyone, including both internal and external devices and users. This approach addresses the risk of insider threats and adapts to the fact that identity has become “the new perimeter” as traffic increasingly moves to the cloud and SaaS.

Zero Trust Network Access vs. Legacy VPN

Zero Trust security controls are commonly implemented via ZTNA, which was developed as an alternative to traditional VPNs. VPNs offer unrestricted network access to authenticated users, which introduces the threat of lateral movement once an attacker gains access to the enterprise environment.

In contrast, ZTNA provides access to specific applications on a case-by-case basis. These decisions can be based on least privilege access controls and corporate policies, as well as other factors, such as device posture checks.

What Do We Mean by AI Zero Trust Security?

AI Zero Trust security applies AI and machine learning to Zero Trust architectures in order to evaluate risk and make policy decisions in real time. It extends Zero Trust principles, adding AI to enhance risk scoring and protocol enforcement at scale.

How AI Plugs Into Zero Trust Components

AI systems integrate with existing elements of an organization’s Zero Trust architecture, including identity management, user and entity behavioral analytics (UEBA), PTNA policy engines, and security analytics platforms. The system will train on historical events and analyst decisions, helping it to refine future decisions to minimize false positive and false negative detections. When implementing AI for Zero Trust, high-quality data from identity providers, endpoints, and network tools is essential to maximize effectiveness and accuracy.

How Does AI Enhance Zero Trust Network Access (ZTNA)?

AI enables ZTNA tools to enhance their decision-making by intelligently evaluating user, device, and session context across an entire session. With AI, enterprises can move beyond static policies to consider deviations from normal login times, application usage, and location to identify anomalies and calculate risk scores. These risk scores can then be used to trigger remediation actions, such as step-up authentication, denied access, or restricted privileges.

AI-Driven Device and Session Posture Checks

Zero Trust architectures have the ability to consider a wide range of potential factors when calculating a risk score for a user session. Common examples include:

• OS version
• Patch level
• EDR status
• Device compliance

Integrating AI makes it possible to track patterns and identify anomalies across the entire organization, rather than a single device. Additionally, they can consider a wide range of factors when calculating a risk score, rather than a single data source. This additional context can enhance threat detection and help tailor remediative action to the situation.

Continuous Verification and Adaptive Access

AI enhances an organization’s ability to perform continuous verification and implement adaptive access across a user session. By monitoring the session in real-time and considering various data points, an AI can continuously update its calculated risk score based on user activity and other warning signs.

If the session becomes too risky, the AI can then take action to resolve this issue. For example, step-up authentication may be used to verify the user’s identity, requiring them to provide a password or MFA code to continue their session. Alternatively, the system may restrict access to high-risk systems or resources or terminate the session entirely.

Using AI for this also provides the security team with insight into the decision process and the cause for escalation. If the AI can explain the history of risk scores and the reasoning behind them, security personnel can take action accordingly, potentially initiating incident response or addressing the false positive from the AI model.

How Does AI Improve Identity and Access Management?

Identity and access management (IAM) implements user authentication, authorization, and auditing for an organization. It is a critical component of a Zero Trust architecture, managing the user profiles and policies for user trust.

AI enhances IAM by performing intelligent identity management and analytics. In addition to threat management, AI can help to cluster similar user roles for role-based access control (RBAC), identify unused accounts, and help identify privileges that may violate least privilege access policies.

While AI can be useful for IAM, it doesn’t replace human oversight. IAM teams should validate AI recommendations instead of automatically approving changes.

Risk-Based Authentication and Step-Up Controls

Risk-based authentication assigns risk scores to sessions and takes action based on these scores. For example, sessions might be flagged if they involve unknown devices, unusual locations, or impossible travel. These sessions must then perform step-up authentication, where additional authentication steps are required to verify the user’s identity.

AI models can be used to more intelligently and adaptively adjust risk scores for a user session. This can consider user behavior, threat trends, and other information to determine where additional verification makes sense while minimizing friction within the user experience.

Identity Lifecycle and Privilege Management

AI can help automate traditionally manual processes for account management. This includes account provisioning, access review, and deprovisioning.

For example, AI systems can identify similar accounts within the organization and compare their privileges, helping to detect privilege creep and other issues. Monitoring user activity can help to identify unused accounts or unnecessary access granted to a role.

What Use Cases Define AI-Driven Zero Trust in Practice?

AI-driven zero trust applies best in situations where risk calculations are complex and can change rapidly. Some common examples include:

• Securing remote employees using unmanaged networks.
• Protecting privileged access to admin consoles.
• Controlling access to AI applications and models.
• Detecting insider threats and account takeovers.

For these applications, AI has the potential to rapidly identify and shut down threats. However, false positive detections can occur, especially when the AI system is still learning and tuning its model. Additionally, AI can overlook threats, so it should complement security teams and traditional controls, rather than replacing them.

Key Design Principles for AI Zero Trust Security

Best practices for implementing AI zero trust security include:

• Policy-First Thinking: Before implementing AI, organizations should clearly define zero trust policies and risk models. This helps to ensure that AI systems are implemented and tuned to align with corporate governance.
• Defense in Depth: AI complements zero trust; it doesn’t replace it. AI-based risk scoring should be layered on top of traditional zero trust security controls implemented via ZTNA, such as least privilege access management and network segmentation.
• Visibility and Explainability: AI should not be relied upon to make access decisions in isolation. Security teams should have visibility into the AI’s thought process and the ability to review changes made based on the AI’s recommendations before they are implemented.
• Privacy and Compliance: AI systems implementing IAM will monitor and analyze user behavior. This must be implemented in accordance with regulatory requirements, and sensitive user data should be appropriately secured against unauthorized access, use, or disclosure.
• Change Management: Small changes to AI systems can have significant impacts on their effectiveness. Modifications should be controlled via a change management process that enforces incremental changes, monitoring, and tuning.

Data, Model, and Policy Governance

Data governance is critical to AI systems; bad training data or telemetry can cause AI to make mistakes and cause harm to the business. Key elements of an AI governance program include:

• Data classification
• Data minimization
• Data access management
• Logging of AI model parameters (training data sources, update cadence, etc.)

IT environments and the AI regulatory landscape are both rapidly evolving. Enterprises must ensure that corporate AI policies align with regulatory expectations regarding transparency, auditability, security, reliability, and data privacy.

What Are the Risks and Limitations of AI in Zero Trust?

Introduction of AI-based systems into identity management introduces a variety of potential risks, including false positives, false negatives, model drift, bias, and potential evasion. For this reason, overreliance on AI-based tools using opaque models can create significant threats without human review.

Attackers can take a variety of different approaches to game AI-based security systems. Examples include:

• Gradually changing behavior to normalize malicious activity
• Poisoning training datasets or telemetry
• Exploiting blind spots

Implementing AI in zero trust can also create operational risks, such as:

• Model misconfigurations
• Noisy alerts
• Misaligned thresholds that cause access disruptions
• Stale AI models

An effective AI-based system requires ongoing training, tuning, and validation as applications, user behavior, and patterns evolve. AI should be carefully implemented and used within a well-governed zero trust program, not act as a replacement for it.

Guardrails and Human-In-the-Loop Controls

AI is a useful tool, but it isn’t perfect. High-impact changes recommended by AI should be reviewed and approved by human analysts to reduce the potential impacts of AI hallucinations and other errors.

The organization should also implement AI change management programs to manage risk and model performance. Best practices include:

• Canary deployments
• A/B testing
• Rollback plans for AI-driven policy changes.
• Tracking AI KPIs, such as reduced incident rates, improved detection speed, and user experience impact.
• Documented decision playbooks specifying which actions can be automated or always require human review

FAQs about AI Zero Trust Security

Is AI zero trust security a product or an architecture?

AI zero trust is an architecture, enhancing the existing zero trust security framework with AI. Organizations looking to enhance existing systems with AI can adopt capabilities incrementally over time, aligning them with existing ZTNA, IAM, and security analytics investments.

How is AI zero trust different from traditional Zero Trust?

Traditional zero trust relies on static, policy-based decisions to apply zero trust principles, such as least privilege access and explicit verification. AI zero trust adds AI to this, offering continuous, risk-based decisions based on contextual data collected from identity systems, endpoints, and the network.

Do we need AI for zero trust to work?

No, traditional zero trust doesn’t use AI, using static policies that enforce least privilege access controls. However, AI is a useful addition to a zero trust architecture, offering more granular control over access management at scale.

What skills does a security team need to run AI zero trust?

Teams implementing AI zero trust need to provide high-quality data to the model, define and enforce governance policies, and implement feedback loops for continuous learning. This requires expertise in IAM governance, data engineering, and policy design.

How Should You Evaluate AI Security Software for Zero Trust?

AI zero trust enhances the effectiveness of Zero Trust architectures by integrating continuous, intelligent risk scoring and analysis. AI security software for zero trust should offer:

• Support for various data sources (identity providers, endpoints, network, etc.)
• Integration with security solutions for policy enforcement
• High-quality, explainable risk scoring
• Tunable policies and governance
• Logging and reporting in accordance with regulatory requirements
• Privacy-preserving telemetry collection and analysis
• Ease of deployment and integration

AI isn’t a necessary part of a zero trust architecture, but it can be a force multiplier when implemented correctly. When evaluating solutions, request concrete examples of AI-driven decisions, including details of data sources, explanations of decisions, and how policies are enforced. AI zero trust security makes zero trust more dynamic and resilient in complex environments. However, it depends on solid architecture, governance, and human oversight.