Building Trust in AI: Keeping Humans in Control of Cybersecurity

TL;DR:

  • Position AI as decision-support tool, not autonomous executor
  • Prioritise transparency, visibility, and responsible use cases
  • AI functions as “smart intern” reducing information overload
  • Maintain human judgment for mission-critical security decisions

Rekha Shenoy, CEO of BackBox, articulates a practical framework for integrating artificial intelligence into cybersecurity operations: treat AI as a complementary tool that strengthens human expertise rather than replacing it. This perspective challenges the prevalent narrative of AI as autonomous solution provider.

Decision Support, Not Decision Making

Shenoy’s central argument positions AI systems to “recommend rather than execute” in security contexts. This distinction proves critical in mission-critical environments where incorrect automated actions could compromise infrastructure or expose sensitive data.

The framework acknowledges AI’s computational advantages—rapid pattern recognition, processing vast datasets, identifying anomalies across distributed systems—whilst recognising that final security decisions require human judgment. Algorithms can flag suspicious activity; humans determine whether flagged behaviour represents genuine threats or acceptable operational variance.

Transparency and Visibility Requirements

Effective AI deployment in cybersecurity demands what Shenoy terms “transparency, visibility, and responsible use cases.” This means understanding how AI systems reach conclusions, maintaining clear audit trails of recommendations, and limiting deployment to scenarios where automated analysis genuinely adds value.

The transparency requirement addresses a fundamental challenge: security teams cannot trust recommendations they cannot verify. If an AI system flags particular network traffic as suspicious, operators need visibility into which patterns triggered the alert and whether those patterns align with known threat indicators.

The Smart Intern Analogy

Shenoy’s characterisation of AI as a “smart intern” provides useful framing for organisational deployment. Like capable junior staff, AI systems can reduce information overload by preprocessing data, identifying patterns, and surfacing relevant findings. This accelerates analysis whilst preserving senior expertise for complex judgment calls.

The analogy also highlights appropriate limitations: you wouldn’t authorise an intern to make critical security decisions independently, regardless of their analytical capabilities. Similarly, AI recommendations should flow through experienced professionals who understand operational context, business requirements, and acceptable risk thresholds.

Practical Implementation

This framework suggests several practical deployment principles:

Supervised Automation: Allow AI to automate routine analysis and preliminary classification, but require human approval for consequential actions like blocking traffic or quarantining systems.

Explainable Outputs: Implement AI systems that provide reasoning alongside recommendations, enabling security teams to validate conclusions against their own expertise.

Continuous Validation: Regularly audit AI recommendations against actual threat outcomes to identify drift, bias, or deteriorating accuracy.

Defined Boundaries: Clearly specify which security functions benefit from AI assistance versus those requiring purely human judgment.

Looking Forward

As AI capabilities advance, the temptation to increase automation in security operations will intensify. Shenoy’s perspective suggests that sustainable deployment requires resisting this impulse—maintaining human control whilst leveraging AI’s analytical strengths.

The organisations most likely to succeed will be those that treat AI integration as an augmentation strategy rather than a replacement programme. This means investing in both AI capabilities and the human expertise necessary to use those capabilities responsibly.

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