Why Your AI Workforce Requires the Same Security Protocols as Human Staff

The New Digital Employees: AI Agents Demand Equal Security Measures

As artificial intelligence becomes increasingly integrated into organizational workflows, security leaders are recognizing that AI agents require the same rigorous security protocols as human employees. According to cybersecurity expert Meghan Maneval, organizations must extend their existing security frameworks to cover AI systems with the same diligence applied to staff members.

“Whether you’re dealing with traditional machine learning algorithms, generative AI applications or AI agents, treat them like any other employees,” Maneval emphasized during recent security discussions. This paradigm shift represents a fundamental change in how organizations should approach AI security management., according to industry reports

Comprehensive Security Training for AI Systems

Just as organizations conduct background checks and security training for new hires, AI systems require similar vetting processes. “You probably do a background check before anyone is hired. Do the same thing with your AI agent,” Maneval advised. This includes evaluating the AI’s training data, development processes, and potential vulnerabilities before deployment.

The security controls that govern human staff access should equally apply to AI agents. Role-based access controls (RBAC), zero-trust architectures, and approval workflows that limit human staff privileges must extend to AI systems. “You know that humans can’t go and do whatever they want across your network and that their navigation within your system is bound by zero trust controls. Well, neither should that AI agent,” Maneval told security professionals., according to market insights

Establishing Organizational Risk Tolerance for AI Deployment

Different organizations will approach AI risk management with varying levels of conservatism. “Some companies may take on more risk. My company, for instance, strongly encourages us to use AI. Other companies might be more conservative or want more policies,” Maneval observed during her presentation., as as previously reported

The foundation of effective AI security begins with understanding existing risk tolerance levels. “The bottom line is that you have to start with what are you already doing, and what are you willing to accept and that turns into your policy statement, which you can then start to build controls,” she explained. This approach ensures that AI security measures align with overall organizational risk management strategies., according to market analysis

Combating AI Drift Through Multi-Technique Monitoring

One of the most significant challenges in AI management is addressing performance degradation over time. AI drift describes the gradual decline in an AI model’s performance due to changes in real-world data, user behavior, or environmental factors.

Maneval used a powerful analogy to illustrate the problem: “This is just like a store tracking four cartons of milk but never checking if they’re spoiled. AI systems often monitor outputs without assessing real-world usage or quality. Without proper thresholds, alerts and usage logs, you’re left with data that exists but isn’t actually useful – just like milk no one wants to drink.”

She strongly advocated for combining multiple monitoring techniques to detect and address AI drift proactively. This comprehensive approach ensures that AI systems maintain their accuracy and relevance throughout their operational lifespan.

Implementing Comprehensive AI Audit Programs

During her session at a recent cybersecurity conference, Maneval outlined the components of an effective AI audit program. She emphasized that audits should evaluate not only the AI technology and training data but also the outputs and controls built around the system.

AI algorithm audits should assess “the model’s fairness, accuracy and transparency,” while output audits must “look out for red flags, such as incorrect information, inappropriate suggestions or sensitive data leaks.” This dual approach ensures both the technical integrity and practical safety of AI implementations.

Maneval also recommended evaluating security guardrails, access controls, and data leak protection mechanisms embedded within AI systems. “Auditing AI isn’t about calling someone out, it’s about learning how the system works so we can help do the right thing,” she concluded, framing AI audits as collaborative improvement processes rather than punitive measures.

The Future of AI Management: From Tools to Team Members

As AI agents gain access to sensitive data, third-party systems, and decision-making authority, their status within organizations is evolving from mere tools to legitimate team members. This transition demands a corresponding evolution in security practices.

The identification of security gaps represents a critical focus area for organizations implementing AI systems. “Those are the gaps that a lot of people don’t know yet. Identifying those gaps are where people are going to have to focus,” Maneval noted, highlighting the importance of proactive security gap analysis in AI deployment strategies.

By treating AI systems with the same seriousness as human staff and implementing comprehensive security frameworks, organizations can harness AI’s potential while maintaining robust security postures in an increasingly automated business landscape.

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