Quick answer

AI agents are transforming how businesses operate, but their rapid adoption brings serious security challenges. Unlike human users, AI agents are dynamic, short-lived, and rely on programmatic authentication. This makes traditional security systems inadequate. By 2028, 25% of enterprise breaches are expected to involve AI agent misuse, and 80% of companies report agents accessing unauthorized systems. To secure your organization, here are five key practices:

• Assign Individual Agent Identities: Replace shared credentials with unique, verifiable identities for each agent. This improves accountability and reduces security risks.

• Apply Least Privilege Access: Limit agents to only the permissions they need for their tasks to minimize exposure.

• Use Context-Aware Access Controls: Implement dynamic policies like Attribute-Based Access Control (ABAC) to adjust permissions in real time.

• Secure Authentication: Opt for short-lived credentials and automated token management to reduce the risk of credential theft.

• Maintain Detailed Audit Trails: Track every agent action for compliance and quick incident response, while incorporating human oversight for critical decisions.

These measures are essential for protecting sensitive data and maintaining operational security in AI-driven environments. Platforms like Prefactor can help implement these strategies effectively by providing tools tailored for AI agent authentication and monitoring.

Securing AI Agents (A2A and MCP) with OAuth2 – Human and Agent Authentication for the Enterprise

Create Distinct and Verifiable Agent Identities

As AI agents become more sophisticated, securing their operations requires a tailored approach. Each agent must have a unique, verifiable identity to ensure accountability and prevent security gaps. Using generic service accounts or static API keys makes it nearly impossible to track individual agent actions, leaving systems vulnerable to attacks.

The numbers tell a clear story: in 2024, identity-based attacks made up 60% of all Cisco Talos Incident Response cases. Additionally, 23% of IT professionals reported incidents where AI agents exposed credentials. Ayesha Dissanayaka from WSO2 emphasizes this point:

"Treating agents as anonymous, all-powerful entities is a recipe for disaster. We must shift our mindset and begin engineering them as first-class citizens in our digital ecosystem. That begins with giving each agent a unique, verifiable identity."

The key to addressing this issue lies in implementing secure, verifiable identities that are dynamically authenticated without relying on stored secrets. These identities should be ephemeral, existing only for the duration of their needed activity. This significantly reduces the risk of credential theft.

Modern frameworks for workload identity offer a strong foundation for this approach. By utilizing certificate-based authentication, federated identities, and standards like SPIFFE SVID, organizations can move away from passwords entirely. For instance, Google’s Autonomous Workload Identity initiative in 2025 employs certificate-based methods to verify every agent interaction, ensuring both authentication and contextual validation. This shift naturally eliminates the need for shared credentials, which are discussed further below.

Removing Shared Credentials

Shared credentials are a major security liability in AI agent environments. When multiple agents rely on the same API key or service account, it becomes challenging to distinguish between legitimate actions and potential security breaches. Without unique identities, malicious activity can easily go unnoticed.

This lack of distinction also complicates forensic investigations. If individual agents cannot be traced, security teams struggle to assess the full scope of a breach and implement effective solutions. The absence of clear ownership and consistent logging delays post-incident analysis and hampers remediation efforts.

A case study from SPIRL in April 2025 highlights these risks. A payroll agent interacting with HR systems, banking APIs, and tax services could expose sensitive financial data or violate compliance rules if its identity and access are not properly secured. By replacing shared credentials with standards-based workload identities, organizations can achieve real-time authentication across both modern and legacy systems, significantly reducing these vulnerabilities.

The benefits are evident: Okta’s 2025 benchmarks revealed a 92% reduction in credential theft incidents when using short-lived 300-second tokens instead of 24-hour sessions. Assigning individual identities to agents also enables fine-grained access control. This ensures agents operate under the principle of least privilege, with permissions dynamically adjusted based on real-time risk assessments. This limits the potential for lateral movement within systems, enhancing overall security.

Role of Platforms Like Prefactor

Platforms like Prefactor take these identity principles a step further by offering specialized tools for managing AI agent authentication. Prefactor provides MCP-compliant identity management, secure login, and delegated access tailored specifically for AI agents.

Prefactor integrates seamlessly with existing OAuth/OIDC systems while extending their functionality to address the unique needs of AI agents. It allows for rapid provisioning and deprovisioning based on policy-driven rules. Unlike traditional IAM systems, which struggle with the dynamic and temporary nature of AI agents, Prefactor is designed to handle agents that may only exist for short periods.

One standout feature is human-delegated access, which ensures accountability. Prefactor’s delegation model links every agent action back to the human who authorized it, creating a clear chain of responsibility.

Additionally, Prefactor provides agent-level audit trails, capturing detailed logs of actions and the context behind them. This is invaluable for compliance and security monitoring. Multi-tenant support ensures proper isolation between business units or customer environments, while centralized identity management remains intact.

Prefactor also integrates with CI/CD-driven access control, enabling organizations to manage AI agent permissions as code. This approach aligns with the growing trend of infrastructure-as-code, ensuring access policies are consistent, version-controlled, and auditable across both development and production environments.

Apply Least Privilege and Scoped Authorization

Building on the foundation of unique agent identities, applying the principle of least privilege ensures that each agent only accesses what is absolutely necessary. This approach is critical for maintaining secure access control for AI agents. By granting agents only the minimum permissions needed to perform their tasks, organizations can significantly reduce the risk of breaches. When agents are given excessive privileges, even small vulnerabilities can lead to severe security incidents.

Consider this: by 2025, 75% of AI security incidents are predicted to result from unauthorized access. On top of that, the average cost of a data breach has soared to $4.45 million. These numbers highlight the importance of implementing least privilege policies effectively.

To achieve this, organizations should define clear roles for agents and restrict their access to essential resources. This includes specifying API actions, resource identifiers, and ensuring actions are limited to trusted traffic sources. Assigning sponsors or custodians to regularly review and recertify agent access adds another layer of accountability. From here, scoped authorization offers a practical way to refine access control even further.

Setting Up Scoped Authorization

Scoped authorization builds on least privilege by introducing dynamic access policies tailored to specific contexts and tasks. This is especially valuable in multi-tenant environments, where business units need strict isolation but still benefit from centralized management.

The cornerstone of scoped authorization is adopting a policy-as-code approach. Instead of relying on static permissions, organizations should encode governance and security policies. This method enables version control, automated testing, and consistent deployment across systems. By moving enforcement to the agent orchestration layer, organizations gain better oversight and control over access policies.

Prefactor's scoped authorization capabilities address these needs by integrating seamlessly with existing OAuth/OIDC systems while adding features tailored for AI agents. Prefactor dynamically adjusts permissions based on real-time risk assessments, allowing organizations to define fine-grained permissions that adapt to an agent’s context, task, and associated risks.

Key strategies for implementation include using short-lived credentials and temporary privilege escalation rather than static keys. This minimizes the time available for potential misuse of credentials. Additionally, rigorous input validation and rate limiting should be applied to all data and interactions involving AI agents. For instance, capping API requests, throttling database queries, and restricting file access can prevent misuse and ensure system stability.

Prefactor also supports multi-tenant environments by maintaining proper isolation between organizational units while centralizing identity management. Its CI/CD-driven access control allows permissions to be managed as infrastructure-as-code, ensuring consistency, traceability, and auditability across all environments.

Beyond scoped authorization, isolation techniques like sandboxing and network segmentation further strengthen security.

Using Sandboxing and Network Segmentation

Sandboxing and network segmentation are key defense-in-depth strategies that isolate agent activities and minimize damage during security incidents. These methods restrict access and prevent agents from moving laterally within a system.

Network segmentation creates virtual boundaries that control how agents communicate. Through microsegmentation, organizations can limit agents to specific network zones, databases, or services required for their tasks. This reduces the potential impact of a breach by confining it to a smaller area.

Sandboxing takes it a step further by creating isolated environments where agents operate under strict controls. These sandboxes enforce resource limits, API restrictions, and include monitoring tools to detect unusual behavior. Coupled with circuit breakers, sandboxes can automatically halt agent activity if predefined thresholds are exceeded.

Organizations should define clear operating limits, such as maximum transaction amounts, API call frequencies, and data access volumes. When these limits are approached, the system can trigger alerts, require human intervention, or temporarily suspend operations. For high-risk actions - like financial transactions, data deletion, or system configuration changes - a human-in-the-loop approach is essential. This ensures that sensitive operations are explicitly approved by a human before proceeding.

Prefactor enhances these measures with agent-level monitoring and compliance tools. The platform provides real-time insights into agent behavior, flagging anomalies and potential policy violations. Even within sandboxed environments, this continuous oversight ensures agent activities align with organizational security policies and compliance requirements.

Set Up Fine-Grained and Context-Aware Access Controls

Securing AI agents goes beyond basic authentication - it's about reducing risks with smarter, more adaptive access controls. These controls, building on scoped authorization, use real-time data to adjust permissions dynamically. Advanced models like Attribute-Based Access Control (ABAC) and Policy-Based Access Control (PBAC) evaluate multiple factors simultaneously, such as user attributes, device type, location, time, and environmental conditions.

The need for these dynamic systems is pressing. From 2021 to 2024, data breaches surged by 70%, and Gartner forecasts that by 2026, 30% of enterprises will deploy AI agents capable of acting with minimal human oversight. With the average data breach costing $4.35 million in 2022, relying on outdated methods is no longer an option.

Context-aware controls operate in real time, continuously assessing risk and adjusting permissions based on security conditions. For instance, companies with remote work policies often use these controls to block access when employees attempt to log in from unapproved devices or locations.

ABAC and PBAC offer greater precision by combining static roles with dynamic attributes, enabling real-time, context-sensitive access. This approach is particularly effective in today’s complex IT environments.

Organizations typically start with a Role-Based Access Control (RBAC) framework as a foundation, gradually transitioning to more sophisticated models. This progression often leads to adopting Just-In-Time (JIT) access for even tighter control.

Using Just-In-Time (JIT) Access

JIT access shifts the focus from long-term permissions to temporary, task-specific access. Instead of granting agents broad, ongoing access, JIT provisions the exact permissions needed for a task and automatically revokes them once the task is complete. This drastically reduces exposure by limiting access duration.

With 90% of organizations leveraging AI to strengthen cybersecurity, many are incorporating JIT access. AI systems analyze real-time factors like user behavior and device location to assess risk. If the analysis deems an access request safe, permissions are granted temporarily and expire automatically.

This approach also eliminates the challenge of managing thousands - or even millions - of long-lived credentials. Instead, identities are created on demand and retired promptly. Adaptive access control ensures continuous monitoring of session context. If unusual activity or a new location is detected, permissions can be adjusted or revoked immediately.

Tools like JumpCloud and Microsoft Azure Conditional Access help organizations implement these dynamic policies, balancing security with operational efficiency. The goal is to ensure agents can perform their tasks seamlessly while maintaining strict security measures.

Integrating MCP Security Frameworks

To complement fine-grained controls and JIT access, Model Context Protocol (MCP) frameworks provide a unified approach to policy enforcement across systems. MCP standardizes how AI agents interact with external resources, ensuring they can only perform approved actions. This enhances compliance and strengthens operational resilience.

MCP frameworks address a critical challenge: maintaining consistent security policies across diverse systems. By defining clear boundaries - such as approved API endpoints, data sources, and operational parameters - MCP creates a centralized security layer that adapts to local requirements.

For example, SpaceTech Inc. implemented an MCP framework to protect sensitive data related to their Satellite X project. They embedded metadata into training documents and used YAML policy files to enforce access controls. When an agent requests information, the system checks the metadata against the user’s role before granting access.

"By combining metadata tags, YAML policies, and the Access Validator Tool, we ensure that access control is not only descriptive but also enforceable, providing a robust, scalable system to protect sensitive information - much like a 2FA system does for traditional security."

This metadata-driven strategy allows organizations to maintain granular control over AI agent behavior while scaling across systems. Policies can incorporate both static attributes, like user roles, and dynamic factors, such as current risk levels, creating a flexible yet secure framework.

Integrating MCP frameworks requires collaboration between security teams, AI developers, and system administrators. Clear governance structures must be established to create and maintain policies. Regular reviews and updates ensure the framework evolves alongside business needs and emerging threats.

Secure Authentication and Short-Lived Credential Management

Strong authentication protocols are the cornerstone of AI agent security, especially as organizations gear up for a future where AI agents could outnumber human identities 80 to 1 by 2030. Unlike human authentication, machine-to-machine (M2M) protocols for AI agents demand robust, automated credential management.

With the rapid growth of AI agents, secure and automated authentication is no longer optional - it's a necessity. Gartner predicts that by 2026, 30% of enterprises will deploy AI agents capable of operating with minimal human oversight. To safeguard these dynamic AI identities, strong authentication measures are critical.

Modern AI agent authentication largely hinges on the OAuth 2.0 client credentials flow, a system tailored for M2M scenarios. This approach uses cryptographic keys to establish secure connections between agents and services without requiring user input. A key enhancement to this model is the use of short-lived tokens.

Short-Lived Tokens and Revocation Mechanisms

Short-lived credentials, sometimes referred to as ephemeral or dynamic secrets, are designed to expire within minutes or hours, unlike traditional long-term credentials. This significantly reduces the window of opportunity for attackers to exploit compromised credentials.

The benefits of short-lived tokens go beyond basic security. They align with Zero Trust principles, ensuring that every request must be validated with a fresh credential. If suspicious activity arises, these credentials can be revoked immediately, eliminating the need to wait for scheduled rotations. This real-time response is especially crucial for AI agents, which may occasionally exhibit unexpected behaviors.

A practical example of this approach can be seen in HashiCorp Vault's dynamic secrets engines, which automate the creation, distribution, and revocation of credentials. This system generates tokens on-demand and ensures they expire based on predefined policies, removing the need for manual renewals.

Moe Abbas, Senior Engineering Manager and Cloud Governance Lead at Canva, shared his experience with credential incidents:

"We had to stop what we were working on and divert engineers away from priorities."

By adopting short-lived credentials, organizations can avoid such disruptions, reducing the likelihood and impact of credential-related security issues. A good starting point for implementation is to test this approach on a low-risk service or a single CI/CD pipeline before rolling it out more broadly.

Best Practices for Credential Management

Beyond token lifecycles, effective credential management is key to reducing risks. AI agents should dynamically fetch credentials for each tool, avoiding hardcoded secrets or exposing tokens in prompts for large language models (LLMs). Backend services must securely attach credentials at runtime.

Here are some practical steps to strengthen credential management:

• Store tokens securely in encrypted vaults or use ephemeral session credentials that exist only for the duration of specific tasks.

• Use refresh tokens intelligently to renew access tokens without disrupting operations, and keep refresh tokens in secure vaults separate from access tokens.

• Sanitize logs to strip out sensitive information, especially when AI agents generate detailed logs for debugging or auditing purposes.

For example, a JavaScript snippet shows how SupportBot posts messages to a Slack channel using an API token retrieved securely from a credential vault. Instead of hardcoding secrets or relying on user sessions, the bot dynamically fetches the appropriate token based on its identity (agentId) and target service (slack-bot-token).

Organizations should also standardize authentication methods to reduce complexity and limit the attack surface. Tracking measurable outcomes, such as fewer service tickets and reduced risk, can help build internal support for these practices.

Prefactor's Authentication Features

Prefactor simplifies authentication by integrating modern features tailored to AI agents. The platform extends existing OAuth/OIDC systems with capabilities designed specifically for AI-driven environments.

Key features include:

• Multiple authentication methods: Options like SSO, MFA, Magic Links, Passkeys, and Social Login provide flexibility to meet various organizational needs. This ensures AI agents can authenticate using the most suitable method for their use case.

• Scoped authorization: Fine-grained access controls ensure AI agents only get the permissions they need for their tasks, adhering to the principle of least privilege.

• Multi-tenant support: For organizations managing multiple AI agent deployments, Prefactor ensures each tenant has isolated authentication boundaries while benefiting from centralized management.

• Agent-level audit trails: These provide detailed insights into authentication events, helping security teams monitor access patterns and investigate anomalies. This capability supports compliance and strengthens overall security.

Maintain Complete Audit Trails and Human Oversight

Keeping a close eye on AI agents is no longer optional - it's a necessity. With 82% of organizations using AI agents but only 44% implementing security policies, there's a glaring gap between adoption and proper management. Even more concerning, only 52% of enterprises can fully track and audit the data accessed or shared by their AI agents. This lack of oversight leaves nearly half of organizations vulnerable to potential risks.

Setting Up Detailed Audit Trails

Audit trails are essentially logs that record every action an AI agent takes, providing a chronological account of its activities. To make these logs effective, they need to cover the essentials: transaction logging, agent performance metrics, and enriched metadata. Each event should detail critical information like input features, model versions, confidence scores, rationale behind decisions, and any user overrides. This level of detail ensures that when something goes wrong, security teams can pinpoint the root cause quickly.

"If your AI system can't tell you who changed what, why a decision was made, or which model version was used, then it's not audit-ready - no matter how explainable it is."

• Debasish Deb, Author at LinkedIn

To implement these logs effectively, organizations need a solid logging strategy. Options include real-time logging, batch processing, or a mix of both. Real-time logging gives immediate insights but demands more resources, while batch processing is more efficient but delays visibility. A hybrid model often works best - logging critical events in real time while handling routine activities in batches.

Data retention also plays a key role. Using tiered storage policies helps balance cost and accessibility. For example, hot storage can hold recent logs for quick access, while warm or cold storage handles older data at lower costs. This approach ensures compliance without overspending.

Finally, securing the audit trails themselves is crucial. Role-based access controls, encryption, and tamper-evident logging protect these records from unauthorized changes. Without these safeguards, audit trails lose their reliability, making them useless for compliance or investigating incidents.

The Role of Human Oversight

Even as AI agents grow smarter, human oversight remains indispensable. Over 80% of organizations using AI agents have reported unintended behaviors. This highlights why critical decisions still need a human touch.

The challenge is finding the right balance between efficiency and control. By 2026, nearly a third of enterprises are expected to deploy AI agents capable of making decisions independently at machine speed. However, certain situations demand human intervention to avoid costly errors or breaches.

Dynamic access control is a practical way to incorporate human oversight. AI agents can be designed to flag decisions that exceed predefined thresholds for review. For instance, in a ticketing system, refunds above a certain dollar amount could be flagged for human approval. This approach minimizes risks like fraud or unchecked errors.

The financial sector offers another example. An AI agent handling loan applications might misinterpret data or expose sensitive customer information if left unchecked. To prevent this, clear access policies, continuous monitoring, and strict guardrails are essential.

"AI agents are designed to adapt dynamically to changing environments, making hardcoded logic not just obsolete, but impossible."

• PwC

Regular assessments of AI decisions compared to human ones can help spot gaps and refine the balance between automation and oversight. Additionally, having well-documented and tested escalation paths for critical decisions ensures smooth handoffs when human intervention is needed.

Prefactor's Monitoring and Compliance Capabilities

Prefactor's tools are designed to bring these principles to life, ensuring effective oversight in dynamic AI environments. By integrating centralized, agent-level audit trails, Prefactor supports compliance and enhances security incident response.

When suspicious activity occurs, Prefactor enables security teams to quickly trace the sequence of events, identify affected systems, and assess the scope of any breaches. This is particularly important as 75% of AI security incidents by 2025 are expected to result from unauthorized access.

Prefactor also addresses challenges faced by organizations managing multiple AI deployments. Its multi-tenant support ensures isolated audit boundaries while allowing for centralized management. By extending existing OAuth/OIDC systems with AI-specific capabilities, Prefactor blends seamlessly into established security frameworks.

For businesses concerned about the average cost of data breaches reaching $4.45 million, Prefactor offers critical protection. Its ability to track and audit all AI agent activities helps close the security gaps that leave many enterprises exposed.

"Audit trails for agents provide the critical transparency and accountability that modern businesses need to monitor, secure, and optimize their AI-powered workflows."

• Adopt AI

Conclusion and Key Takeaways

Securing AI agents requires a fresh perspective. With 96% of executives expecting a moderate to significant rise in AI agent adoption over the next three years, organizations must prioritize agent security from the start.

The outlined practices create a solid framework for improving accountability, reducing risks, and adjusting to evolving operational demands. Assigning unique agent identities eliminates shared credential vulnerabilities and ensures clear accountability. Least privilege authorization limits agents to only the access they need, shrinking the attack surface. Context-aware, fine-grained controls allow systems to adapt to changing agent requirements. Secure authentication with short-lived credentials minimizes risks from compromised tokens. Lastly, comprehensive audit trails paired with human oversight offer the transparency and control necessary for compliance and incident response.

"Securing AI agents means preparing for software that thinks, adapts, and sometimes surprises you. It's a different game - and it demands a different playbook." - Maria Paktiti

These best practices are essential to counter increasingly complex risks. In 2023, 65% of data breaches involved internal actors, with human error playing a role in 68% of cases. The autonomy and access granted to AI agents can magnify these risks if not carefully managed.

Prefactor provides a strong example of how to address these challenges. Its purpose-built authentication and audit tools simplify agent access management while integrating seamlessly with existing systems. By adopting such solutions, organizations can implement these practices effectively without disrupting operations.

Striking the right balance between security and efficiency is critical. Adaptive policies, automated controls, and flexible identity management enable organizations to use AI agents safely. Without such measures, companies risk becoming part of the growing list of security breaches.

Looking ahead, with nearly 70% of companies planning to increase investment in AI governance over the next two years, now is the time to act. Implementing these best practices today not only strengthens current security but also prepares organizations for future challenges and regulations.

FAQs

• What are the key security risks of AI agents, and how can access control help protect them?AI agents encounter a variety of security threats, including prompt injection attacks, agent hijacking, unauthorized data access, and supply chain vulnerabilities. These risks can expose sensitive information, disrupt operations, or even enable compromised agents to perform harmful actions.To address these challenges, it's crucial to adopt several protective measures:Implement least privilege access to restrict agents to only the data and functions they absolutely need.Use continuous authentication and real-time risk assessments to quickly identify and respond to suspicious behavior.Enforce strict permission management and conduct regular monitoring of agent activity to ensure adherence to security protocols.By following these steps, organizations can strengthen their defenses against potential threats while keeping operations running smoothly.

• What is the principle of least privilege access, and how can it improve AI agent security?The concept of least privilege access revolves around granting AI agents only the permissions they absolutely need to complete their tasks. This approach reduces the chances of misuse or unauthorized actions, strengthening overall security.To put this into practice, start by crafting minimal access policies that align with each agent’s specific responsibilities. Incorporate fine-grained authorization to control precise permissions and use micro-segmentation to keep sensitive systems isolated. Make it a habit to regularly review and monitor these permissions, ensuring they stay relevant and adjusting them when necessary to avoid unnecessary access. These measures create a safer and more controlled environment for managing your AI agents.

• Why are detailed audit trails and human oversight critical for managing AI agents, and how can organizations implement these effectively?Detailed audit trails and human oversight play a key role in maintaining transparency, accountability, and compliance within AI systems. These measures not only help in spotting potential errors and preventing misuse but also build trust by offering clear documentation of AI's actions and decisions.To put these practices into action, organizations should focus on the following:Establish thorough logging protocols to document AI activities and decisions systematically.Conduct regular reviews of AI logs to spot unusual patterns or potential risks.Engage human experts to monitor critical AI operations and step in when necessary.By pairing detailed audit records with consistent human oversight, companies can keep AI systems in check and reduce the risks tied to automation.