Mar 6, 2025
AI agents are transforming industries, but they come with serious security risks like data leaks, system hijacking, and model manipulation. For example, Samsung banned AI tools after employees accidentally exposed sensitive information.
Key Security Steps
Data Protection: Encrypt data, use access controls, and follow standards like ISO 27001 or GDPR.
Access Management: Enforce multi-factor authentication and role-based access.
Code Security: Automate testing, update dependencies, and review code regularly.
System Monitoring: Track AI activities, detect anomalies, and set up real-time alerts.
Emergency Protocols: Have a clear plan for containment and investigation after incidents.
Quick Fact: Companies conducting regular AI security audits see a 65% drop in breaches.
This checklist helps secure AI agents while keeping them productive. Let’s explore how to safeguard your systems effectively.
Security Planning and Risk Analysis
Common Security Threats
Organizations face several categories of AI security threats. Address each threat as follows:
Data Exposure – Unauthorized access to sensitive information; mitigate with data loss prevention (DLP), encryption, and access controls.
Model Manipulation – Tampering with AI model integrity; mitigate with regular testing and monitoring for model drift.
System Exploitation – Misuse of system resources; mitigate with network segmentation and resource limits.
Authentication Bypass – Unauthorized access to AI functions; mitigate with multi-factor authentication and role-based access.
Chain Effect Vulnerabilities – Cascading failures within interconnected systems; mitigate with system isolation and compartmentalization.
Recent data shows that organizations conducting regular AI security audits see a 65% drop in successful breaches targeting their AI infrastructure (2).
Creating a Security Plan
A solid security plan is essential to tackle vulnerabilities and ensure ongoing protection. Avivah Litan from Gartner explains in these words of insight:
By viewing and mapping all AI agent activities, detecting and flagging anomalies, and applying real-time remediation, businesses can harness the power of AI agents while maintaining robust security measures. In this rapidly evolving landscape, proactive risk management is not just an option – it is a necessity. (1)
An effective security plan should include:
Initial Assessment and Mapping: Document the role of each AI agent, its access levels, and its integration with other systems. Map data flows to identify weak points.
Risk Assessment Framework: Use frameworks like MAESTRO designed for AI vulnerabilities (5).
Access Control Implementation: Implement role-based access control (RBAC), enforce time-limited access, and use multi-factor authentication along with API request signing.
Monitoring and Response Protocol: Deploy tools that detect anomalies in real time, suspend suspicious activities, flag issues for escalation, and track system interactions.
Isolation and Containment: Create security zones by separating AI agents from production systems, segmenting networks, and maintaining isolated test setups.
Google’s SAIF Risk Assessment tool is a great resource for evaluating your security practices (4). Heather Adkins, VP of Security Engineering at Google, notes:
The SAIF Risk Assessment is an interactive tool for AI developers and organizations to take stock of their security posture, assess risks and implement stronger security practices. (4)
Conduct security assessments every quarter (2) and maintain continuous monitoring to quickly address suspicious activities (1).
Basic Security Requirements
Data Security Standards
Protect data from unauthorized access, disruption, or modification by following established frameworks. Consider these standards:
ISO 27001 – Manages information security through regular audits, risk assessments, and documented procedures.
NIST SP 1800 – Provides technical security guidelines using encryption protocols, access management, and monitoring systems.
SOC 2 – Ensures data privacy and security with enforced access controls, encryption, and continuous monitoring.
GDPR/CCPA Compliance – Requires managing user consent, minimizing data collection, and prompt breach notifications.
Key actions to prioritize:
Encrypt data both at rest and in transit.
Maintain detailed access logs and clear data labeling.
Perform routine security audits to identify vulnerabilities.
Stay updated on compliance requirements and adapt as needed.
User Access Controls
Regulating system access is critical. Ensure secure access by:
Role-Based Access Control (RBAC): Assign roles by job responsibilities, apply time-based access restrictions, review and update permissions regularly, and automate user provisioning.
Multi-Factor Authentication (MFA): Require MFA for all AI system access. As experts note, "MFA is considered a core component of a strong identity and access management (IAM) framework" (6).
Code Security Guidelines
Research from NYU shows AI-assisted code can be three times more prone to flaws compared to manually written code (7). Improve code security by:
Automated Testing – Scan for vulnerabilities and check dependencies with daily automated scans.
Code Review – Conduct both manual and peer reviews via pre-deployment assessments.
Dependency Management – Monitor versions, track vulnerabilities, and update on a weekly basis.
Container Security – Scan images, use immutable tags, and enable continuous monitoring.
One financial services company reduced its security risks by 75% in just three months using Veracode Fix (7). As a reminder:
"Security needs to be a priority as you develop code, not an afterthought." (7)
Key practices include integrating security during development, using automated scanning tools, regularly updating dependencies, handling exceptions securely, and enforcing strong authentication.
Security Operations
System Monitoring
Implement systems that monitor AI agent activities in real time to ensure secure and efficient operations. Consider these focus areas:
Activity Logging – Track all AI actions by recording timestamps, user interactions, and system changes.
Anomaly Detection – Identify unusual behavior using machine learning compared against baseline behaviors.
Performance Metrics – Monitor system health, including response times, resource usage, and error rates.
Access Tracking – Record both successful and failed authentication attempts and session data.
Effective monitoring requires setting up automated response systems, establishing baseline behavior, integrating monitoring tools with your security framework, and closely tracking performance metrics. Use emergency response steps if any issues arise.
Emergency Response Steps
When a security incident occurs, take the following actions:
Initial Assessment: Collect and document all details including timestamps, affected systems, impact, and monitoring data.
Containment Protocol: Quickly disable affected AI agents or restrict access to compromised systems.
Investigation Process: Analyze access logs, authentication attempts, infrastructure changes, data alterations, and any traces of unauthorized access.
Addressing the immediate threat is only the first step; ongoing maintenance ensures long-term security.
System Maintenance
Regular maintenance is essential for both security and performance. Focus on these tasks:
Security Patches (Weekly): Apply updates and verify their integrity.
Access Review (Monthly): Audit permissions and adjust access controls.
Model Verification (Bi-weekly): Check for unauthorized changes.
Compliance Audit (Quarterly): Ensure ongoing compliance and update documentation.
Other important actions include consistent monitoring of performance and security metrics, validating compliance with industry standards, analyzing incident logs for recurring issues, and regularly testing emergency response protocols.
Rules and Requirements
Legal Requirements
Under Executive Order 14110 (October 30, 2023), organizations using AI agents must comply with specific federal security standards. Key requirement areas include:
System Testing – Regular evaluation of AI systems focused on safety, reliability, and standard testing protocols.
Risk Mitigation – Pre-deployment risk assessments to protect cybersecurity and infrastructure.
Information Sharing – Ongoing reporting for dual-use models covering development activities and training data.
Content Authentication – Using labeling mechanisms to identify AI-generated content.
Safeguarding critical infrastructure also involves addressing risks tied to AI and CBRN (Chemical, Biological, Radiological, and Nuclear) threats. These guidelines serve as a baseline for internal policies.
Security Policies
Internal security policies should transform federal guidelines into daily operational steps. Focus on:
Risk Assessment – Regular vulnerability scanning through routine system evaluations.
Data Protection – Implement encryption protocols and adopt Zero Trust security principles.
Access Control – Enforce policies with regular authorization reviews.
Incident Response – Establish plans for detection, containment, ongoing monitoring, and audits.
As noted by Qualys Security Blog:
"Clear guidelines for AI security ensure that systems are deployed responsibly, risks are minimized, and compliance with industry regulations is maintained. Proactive planning is key to leveraging AI safely and effectively." (10)
Security Training
Effective training programs empower staff to apply security measures consistently. Consider the following approaches:
Foundational Knowledge: Provide basic training on AI systems, potential risks, security protocols, and data privacy measures (11).
Role-Specific Training: Offer tailored training modules based on job roles and interactions with AI systems (11).
Ongoing Education: Conduct regular refresher courses and hands-on exercises to address emerging threats and improve incident response skills (12).
"Artificial intelligence (AI) holds extraordinary potential for both promise and peril. Responsible AI use can help solve urgent challenges while making our world more prosperous, productive, innovative, and secure." (9)
Ensure clear reporting channels for security concerns and maintain detailed, regularly updated training records.
Next Steps
Security Checklist Summary
Regular security assessments are crucial for protecting AI systems. Organizations that perform structured audits have seen a 65% drop in successful breaches (2). Focus on these areas:
Monitor Activity – Real-time threat detection and logging (Daily).
Test Vulnerabilities – Penetration testing and assessments (Quarterly).
Review Compliance – Ensuring regulatory alignment and consistent policies (Bi-annually).
Evaluate Training – Conduct security awareness and response drills (Monthly).
Security Updates
Alongside regular assessments, continuously updating security measures is crucial. As AI systems grow more advanced, challenges in securing them also evolve. As Varun Kumar, Content Specialist, states:
Securing AI systems is a never-ending task – it just never ends. Since AI continues to evolve, we must continue to revise how we secure it. (3)
Key steps to strengthen your AI security include:
Regular Security Audits: Identify vulnerabilities, verify access controls, and review data protection measures, while keeping detailed activity logs for anomalous patterns (8).
Continuous Monitoring: Deploy advanced monitoring tools and run regular incident response drills (8).
Compliance Updates: Revise security protocols to meet emerging AI regulations and standards (13).
Staying proactive with these strategies will help mitigate risks and effectively protect your AI systems.