Enterprise AI Analysis
Augmented Intelligence Framework for Human-Artificial Intelligence Teaming in Cybersecurity
Authored by Masike Malatji, Published on 13 June 2025
Executive Summary
As cyberattacks grow more complex, organisations face critical challenges. Traditional human-centric defences are insufficient, necessitating a collaborative approach integrating human intelligence with AI. This paper introduces the cybersecurity Augmented Intelligence Framework (cAIF), a conceptual framework designed to optimise human-AI teaming (HAIT) in cybersecurity. Augmented intelligence aims for AI to enhance, rather than replace, human intelligence through harmonious working relationships. The cAIF is a hybrid intelligent framework with five core components: Decision-Making Matrix, Paradigm Allocation Engine, Task-Specific Modules, Feedback and Learning System, and Interoperability Framework. The framework enhances human-AI collaboration, integrates human insights with AI capabilities to improve resilience and adaptability against evolving cyber threats. Future research should focus on empirically validating the cAIF in various cybersecurity domains like healthcare and finance.
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
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HMI Paradigms Identified
cAIF Decision-Making Process
Cybersecurity task identified
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Task complexity analysis
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Urgency & risk level analysis
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Decision-Making Matrix
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HMI paradigm selected
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Paradigm Allocation Engine
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HMI paradigm allocated
| Paradigm | Strengths | Suitability |
|---|---|---|
| Human-in-the-Loop (HITL) |
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| Human-on-the-Loop (HOTL) |
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| Human-Out-of-the-Loop (HOOTL) |
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| Human-Alongside-the-Loop (HATL) |
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| Human-in-Command (HIC) |
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| Coactive Systems |
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Empirical Validation & Future Directions
The paper outlines a plan for empirical validation of the cAIF. This involves case studies in sectors like healthcare and finance, simulations using cyber ranges (e.g., MITRE ATT&CK), and pilot implementations. Future research will focus on developing advanced AI/ML tools, contextual AI for dynamic paradigm switching, integrating XAI, applying the framework to other domains (healthcare, financial services), and improving human factors and user experience through user-centric design and trust studies. Addressing ethical and legal considerations, particularly concerning AI autonomy, privacy, and accountability, is also a key area.
- Case studies: Gather qualitative and quantitative data on SME system performances (e.g., incident detection accuracy, time-to-response) before and after cAIF implementation.
- Simulations: Deploy cAIF components in controlled environments (cyber ranges) to assess real-time performance against common cybersecurity challenges like ransomware or insider threats.
- Pilot implementations: Phased deployment in real-world settings, starting with specific operations (e.g., incident response), then expanding to multi-layered defence strategies, incorporating human operators' and supervisors' feedback.
- Advanced AI/ML tools: Develop contextual AI for dynamic paradigm switching and integrate XAI tools for transparency.
- Cross-domain adaptability: Research application in healthcare and financial services, identifying universally applicable core components.
- Human factors: Focus on user-centric design, intuitive interfaces, and trust studies.
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Strategic cAIF Implementation Roadmap
Adopting the cAIF requires a phased, strategic approach to ensure seamless integration and maximum impact.
Phase 1: Assessment & Pilot (3-6 Months)
Conduct a thorough assessment of current cybersecurity posture and identify high-priority areas for cAIF integration. Implement a pilot project in a specific, contained domain to gather initial data and refine the framework.
Phase 2: Gradual Integration & Training (6-12 Months)
Expand cAIF deployment to additional cybersecurity operations. Invest in comprehensive training for human operators on human-AI teaming best practices and new AI/ML tools. Begin incorporating XAI mechanisms.
Phase 3: Full-Scale Deployment & Continuous Optimization (12+ Months)
Achieve full integration across the enterprise's cybersecurity infrastructure. Establish continuous learning loops and feedback systems for ongoing optimization. Explore cross-domain adaptability and advanced AI/ML capabilities.
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