{"id":28831,"date":"2026-02-16T08:00:00","date_gmt":"2026-02-16T07:00:00","guid":{"rendered":"https:\/\/pegamento.nl\/niet-gecategoriseerd\/what-security-measures-are-required-in-agentic-ai\/"},"modified":"2026-06-03T22:41:58","modified_gmt":"2026-06-03T20:41:58","slug":"what-security-measures-are-required-in-agentic-ai","status":"publish","type":"post","link":"https:\/\/pegamento.nl\/en\/agentic-ai\/what-security-measures-are-required-in-agentic-ai\/","title":{"rendered":"What security measures are required in Agentic AI?"},"content":{"rendered":"

Agentic AI requires extensive security measures because of the autonomous decision-making power of these systems. Key measures include technical security such as encryption and access controls, AVG and AI Act compliance, privacy protection through data minimization, and continuous monitoring of AI behavior. This integrated approach protects against risk while maintaining the benefits of autonomously acting AI assistants. <\/p>\n

What are the biggest security risks in Agentic AI?<\/h2>\n

The primary security risks in Agentic AI are data leakage<\/strong> through uncontrolled access to sensitive information, unauthorized system access via compromised AI agents, model poisoning in which malicious actors manipulate AI behavior, and autonomous decisions without human oversight that can have unwanted consequences.<\/p>\n

Data leakage poses the greatest risk because Agentic AI needs access to business-critical information to act effectively. Without adequate security measures, AI agents may inadvertently share sensitive customer data, financial information or strategic business data with unauthorized parties. <\/p>\n

Unauthorized access occurs when cybercriminals use AI agents as a gateway to internal systems. Because of the increased privileges Agentic AI requires for autonomous actions, attackers can gain deeper access than with traditional systems. <\/p>\n

Model poisoning threatens the integrity of AI decisions. Attackers can manipulate training data or use adversarial inputs to make AI agents take wrong actions, which is especially dangerous in financial transactions or customer communications. <\/p>\n

What technical security measures are essential for Agentic AI?<\/h2>\n

Essential technical security measures for Agentic AI include end-to-end encryption<\/strong> for all data exchange, role-based access controls with minimal privileges, comprehensive audit logging of all AI actions, sandboxing for isolation of AI processes, and real-time monitoring systems that detect anomalous behavior.<\/p>\n

Encryption protects data both in transit and at rest. All communications between AI agents and external systems must be encrypted, as well as stored training data and model parameters. This prevents sensitive information from being intercepted during data exchange. <\/p>\n

Access controls restrict AI agents to only necessary system components. Implement the principle of least privilege, allowing each AI agent access only to specific databases, APIs and functions needed for assigned tasks. <\/p>\n

Audit logging records all AI decisions and actions for traceability. This includes timestamps, dates used, decision logic and actions performed. These logs are critical for compliance and incident response. <\/p>\n

Sandboxing isolates AI processes from critical systems. By running AI agents in controlled environments, you limit the impact of potential security breaches or unwanted behavior. <\/p>\n

How do you ensure compliance and governance in Agentic AI?<\/h2>\n

Compliance and governance at Agentic AI require strict compliance with AVG\/GDPR regulations<\/strong>, preparation for the EU AI Act, structured documentation of AI decision-making processes, approval workflows for new AI features and continuous compliance monitoring with automated reporting.<\/p>\n

AVG compliance begins with privacy-by-design principles. Document what personal data AI agents process, why it is needed and how long it is kept. Implement mechanisms for data subject rights, such as access, correction and deletion of data. <\/p>\n

The EU AI Act classifies AI systems by risk level. Agentic AI often falls under high-risk categories, which means you need to implement extensive documentation, risk assessment and human supervision. Prepare for conformity assessment and CE marking. <\/p>\n

Governance frameworks define who is responsible for AI decisions. Establish clear roles for AI development, deployment and monitoring. Create escalation procedures for situations where AI agents act outside established parameters. <\/p>\n

Approval processes ensure that new AI functionality is tested for risk. Implement multilevel reviews, where technical, legal and business stakeholders review new AI capabilities before deployment. <\/p>\n

What are the best practices for data privacy in Agentic AI?<\/h2>\n

Data privacy best practices for Agentic AI include data minimization<\/strong>, collecting only necessary data, anonymization and pseudonymization techniques, secure data storage with geographic control, a privacy-by-design architecture and transparent communication about data usage to customers.<\/p>\n

Data minimization mitigates privacy risks by collecting only relevant information. Train AI agents to identify specific data needed for tasks and automatically ignore or delete irrelevant information after processing. <\/p>\n

Anonymization techniques such as differential privacy and k-anonymity protect individual privacy while preserving AI functionality. Implement these methods especially with training data and analytics, where individual identification is not necessary. <\/p>\n

Secure data storage keeps sensitive information within controlled environments. Choose data centers in the Netherlands or the EU, implement encryption at rest and use secure backup procedures with geographic replication within EU borders. <\/p>\n

Privacy by design integrates privacy protection into every stage of AI development. This means privacy impact assessments for new features, privacy-friendly default settings and proactive privacy controls rather than reactive measures. <\/p>\n

How do you monitor and manage Agentic AI systems securely?<\/h2>\n

Secure monitoring and management of Agentic AI require real-time monitoring<\/strong> of AI behavior and performance, defined incident response procedures, regular security assessments, performance tracking of AI agents, and automated alerts when anomalies or security events occur.<\/p>\n

Real-time monitoring detects unusual AI behavior before problems escalate. Implement dashboards that visualize AI decisions, response times, error rates and resource utilization. Set thresholds for automatic alerts for anomalies. <\/p>\n

Incident response procedures define the steps in security events. This includes isolation of AI agents, forensic examination of logs, impact analysis and communication to stakeholders. Practice these procedures regularly with tabletop exercises. <\/p>\n

Security assessments evaluate AI systems for emerging vulnerabilities. Perform monthly vulnerability scans, test penetration scenarios specific to AI components, and review access controls and permissions. <\/p>\n

Performance tracking monitors the effectiveness of AI and detects degradation that may indicate security risks. Track accuracy metrics, decision confidence scores and user satisfaction to identify potential model drift or manipulation. <\/p>\n

How Pegamento helps with secure Agentic AI implementation?<\/h2>\n

We offer a security-first approach for secure Agentic AI deployment<\/strong> with ISO 27001-certified processes, Dutch data location and integrated security measures. Our customized solutions combine proven standard building blocks without costly customization, where you can purchase everything under one roof. <\/p>\n

Our safety approach includes:<\/p>\n