5 Myths About AI in Cybersecurity and the Realities of Agentic Security

In recent years, artificial intelligence has become one of the most charged topics in cybersecurity conversations. However, misconceptions about AI's role in security operations continue to circulate. Let's examine five common myths and uncover the realities of how AI, particularly in the context of agentic security, is transforming cybersecurity.

Myth #1: AI in Cybersecurity Means Complete Automation—No Human Involvement Needed

Why People Believe It: The popular narrative around AI often portrays it as a technology that will eventually replace human workers entirely. Stories of AI outperforming humans in various tasks, combined with marketing messages promoting "fully automated" security solutions, have led many to believe that AI-powered security systems can operate independently without human oversight.

The Reality: AI in cybersecurity is best understood as an augmentation of human capabilities, not a replacement. While AI excels at processing vast amounts of data, identifying patterns, and automating routine tasks, human expertise remains crucial for:

• Strategic decision-making and risk assessment
• Understanding business context and priorities
• Investigating complex incidents that require creative problem-solving
• Making final decisions on high-impact security actions
• Developing and adjusting security policies

The most effective security programs embrace a hybrid approach where AI handles the heavy lifting of data analysis and routine tasks, while security professionals focus on strategic initiatives and complex investigations.

Myth #2: Agentic Security is Just SOAR on Steroids

Why People Believe It: Security Orchestration, Automation and Response (SOAR) platforms have been around for years, and many see agentic security as simply a more powerful version of existing automation tools. The similar promises of automated response and workflow optimization contribute to this misconception.

The Reality: Agentic security represents a fundamental shift in how security systems operate. Unlike traditional SOAR platforms that follow pre-programmed playbooks and rules, agentic security systems:

• Demonstrate autonomous reasoning and decision-making capabilities
• Learn and adapt from experience in real-time
• Understand context and can modify their responses accordingly
• Can handle novel situations without pre-programmed responses
• Coordinate complex responses across multiple security tools and domains
• Engage in natural language interaction with security teams

While SOAR platforms excel at automating known workflows, agentic security brings intelligence and adaptability to security operations that goes far beyond simple automation.

Myth #3: AI-Powered Security is Only for Big Enterprises

Why People Believe It: The perception that AI requires massive amounts of data, expensive infrastructure, and specialized expertise has led many to believe that AI-powered security solutions are only practical for large organizations with substantial resources.

The Reality: Modern AI-powered security solutions have become increasingly accessible to organizations of all sizes:

• Cloud-based solutions have dramatically reduced infrastructure costs
• Pre-trained models eliminate the need for extensive historical data
• User-friendly interfaces reduce the need for AI expertise
• Scalable pricing models make solutions affordable for smaller organizations
• Managed security service providers (MSSPs) offer AI-powered security as a service

Small and medium-sized businesses often benefit the most from AI-powered security, as it helps them achieve enterprise-grade security capabilities without maintaining large security teams.

Myth #4: You Can't Trust AI in Cybersecurity Because of Hallucinations

Why People Believe It: High-profile cases of AI systems generating false or inconsistent outputs, particularly in generative AI applications, have raised concerns about the reliability of AI in security contexts. The potential consequences of false positives or missed threats in cybersecurity make these concerns particularly acute.

The Reality: Security-focused AI systems are fundamentally different from generative AI models, employing multiple architectural and training approaches to ensure reliability:

Federated Learning and Distributed Training: One of the most powerful approaches to minimizing hallucinations in security AI is federated learning. This approach:

• Allows multiple organizations to train AI models collaboratively without sharing raw data
• Combines insights from diverse security environments while maintaining data privacy
• Creates more robust models by learning from a broader range of real-world scenarios
• Reduces hallucinations by validating patterns across multiple independent data sources
• Enables continuous model improvement without centralizing sensitive security data

The federated learning approach is particularly effective at reducing hallucinations because of

1. Pattern Validation: When a potential security pattern is identified, it must be consistently observed across multiple participating organizations before being incorporated into the model
2. Anomaly Verification: Unusual behaviors or potential threats are cross-referenced against patterns seen in other environments
3. Local Contextualization: Organizations can fine-tune the shared model to their specific environment while maintaining the benefits of collective learning

Additional Reliability Measures:

• Models employ multiple validation layers and confidence scoring
• They operate within defined constraints and security policies
• They maintain clear audit trails of their decision-making process
• They incorporate human feedback loops for continuous improvement
• They use ensemble approaches combining multiple specialized models
• They implement strict boundary conditions for automated responses

Real-world Implementation: In practice, these systems often use a multi-stage approach:

1. Initial detection using the federated model
2. Local context validation
3. Correlation with other security tools
4. Confidence scoring based on multiple factors
5. Automated response only when confidence thresholds are met

This comprehensive approach makes AI security systems fundamentally more reliable than general-purpose AI models, with hallucination risks effectively mitigated through both architectural design and operational controls.

Myth #5: AI in Cybersecurity Poses Privacy Risks

Why People Believe It: Concerns about AI systems collecting and analyzing vast amounts of data, combined with general privacy concerns about AI, have led to fears about privacy implications of AI-powered security solutions.

The Reality: While privacy concerns are valid, modern AI security solutions are designed with privacy protection as a core feature:

• They can operate on encrypted or anonymized data
• They support data minimization principles by only collecting necessary information
• They incorporate privacy-preserving machine learning techniques
• They comply with major privacy regulations like GDPR and CCPA
• They provide granular controls over data collection and retention
• They can detect and prevent privacy violations by other systems

In many cases, AI actually enhances privacy protection by identifying potential data leaks and privacy violations more effectively than traditional security tools.

Conclusion

As AI continues to evolve in the cybersecurity landscape, understanding these realities helps organizations make informed decisions about implementing AI-powered security solutions. The key is to approach AI not as a magic bullet, but as a powerful tool that, when properly implemented and managed, can significantly enhance an organization's security posture while respecting privacy and maintaining human oversight.