Current Performance and Development Roadmap
We present our systematic development approach with clear performance benchmarks for each model generation. RakshaAI o1 establishes our foundation capabilities, while o2 and o3-lla represent our roadmap toward breakthrough cybersecurity AI performance.
Agent Reasoning and Explainability
One of the key advantages of RakshaAI o3-lla is its ability to provide detailed explanations for its security decisions. The agent architecture maintains a reasoning trace that can be inspected by security analysts, enabling both verification of AI decisions and knowledge transfer from the AI system to human operators.
Example: APT Investigation Reasoning Chain
Introduction
This paper presents our systematic approach to developing breakthrough cybersecurity AI capabilities through a multi-generation model architecture. We begin with RakshaAI o1, our current foundation model that demonstrates strong performance across standard cybersecurity benchmarks, and outline our roadmap to advanced Agent AI capabilities through successive model generations.
RakshaAI o1 (Current): Our foundation model achieves 91.2% accuracy across cybersecurity benchmarks with 1.8% false positive rates. Built on transformer architecture with 12.5B parameters, it establishes our core threat detection and response capabilities.
RakshaAI o2 (In Development): Our next-generation model targeting 96% accuracy with sub-second response times. Will incorporate advanced behavioral analysis and preliminary autonomous response capabilities through 47.3B parameter architecture.
RakshaAI o3-lla (Roadmap): Our target Agent AI system with 175.8B parameters, designed to achieve 99%+ accuracy with full autonomous investigation and response capabilities. This represents our vision for next-generation cybersecurity AI that surpasses human expert performance.
Current Development Status and Investment Opportunity
RakshaAI is currently in active development of our o1 foundation model, with demonstrated capabilities across core cybersecurity functions. Our prototype has successfully completed initial validation on standard benchmarks and is undergoing enterprise pilot deployments with select customers.
Current Capabilities (RakshaAI o1): 91.2% threat detection accuracy, 2.1-second average response time, 1.8% false positive rate. The model successfully identifies known and unknown threats across network, endpoint, and user behavior analytics domains.
Development Timeline: RakshaAI o2 development is projected for 12-18 months, targeting 96% accuracy with preliminary autonomous response capabilities. RakshaAI o3-lla represents our 24-36 month vision for full Agent AI capabilities in cybersecurity.
Investment Requirements
Series A ($15M - 24 months)
- Complete RakshaAI o1 production deployment
- Begin RakshaAI o2 development (47.3B parameters)
- Expand engineering team (25 engineers)
- Scale compute infrastructure
- Enterprise customer acquisition
Series B ($50M - 36 months)
- Launch RakshaAI o2 with autonomous capabilities
- Begin RakshaAI o3-lla Agent AI development
- International market expansion
- Advanced R&D initiatives
- Strategic partnerships and integrations
Model Architecture Evolution
Our development approach follows a systematic scaling methodology, with each model generation building upon proven architectural foundations while introducing new capabilities. RakshaAI o1 establishes our core transformer architecture with cybersecurity-specific optimizations.
RakshaAI o1 Architecture: Built on a 12.5 billion parameter transformer model with specialized attention mechanisms for temporal security events. The model combines supervised learning on labeled security datasets with self-supervised learning on network traffic patterns.
Planned Enhancements: RakshaAI o2 will incorporate graph neural networks for network topology understanding and reinforcement learning components for autonomous response optimization. RakshaAI o3-lla will add multi-agent architectures for complex investigation workflows.
Scaling Performance
Zero-Day Detection
Agent AI Vision: RakshaAI o3-lla Roadmap
Our long-term vision with RakshaAI o3-lla includes sophisticated Agent AI capabilities that enable autonomous multi-step reasoning for complex cybersecurity operations. This represents our roadmap for transforming cybersecurity from reactive monitoring to proactive, intelligent defense.
Planned Agent Architecture: RakshaAI o3-lla will feature multi-agent systems that can maintain context across extended investigation workflows and coordinate responses across multiple systems. Early prototypes demonstrate promising capabilities in autonomous threat hunting scenarios.
Target Capabilities: The agent system is designed to autonomously formulate hypotheses about potential security incidents, design investigation strategies, and execute complex queries across multiple data sources. Our development target is 95%+ autonomous resolution of security incidents.
Real-Time Agent Decision Making
Threat Hunting Agent
Autonomously investigates suspicious activities, correlates indicators across multiple data sources, and builds comprehensive threat profiles using advanced reasoning capabilities.
Response Orchestrator
Coordinates complex incident response workflows, automatically containing threats while minimizing business disruption through intelligent decision-making processes.
Continuous Monitor
Maintains persistent awareness of security posture, proactively identifying vulnerabilities and recommending preventive measures based on threat landscape analysis.
Performance Across Attack Sophistication Levels
We analyzed RakshaAI o3-lla's performance across different levels of attack sophistication, from basic automated attacks to advanced nation-state campaigns and AI-powered attacks. This analysis provides insight into the model's robustness against increasingly sophisticated adversaries.
Notably, RakshaAI o3-lla maintains high detection accuracy even against AI-powered attacks that specifically attempt to evade machine learning detection systems. This suggests that the model has learned robust features that generalize beyond the specific evasion techniques seen during training.
Limitations and Safety Considerations
While RakshaAI o3-lla demonstrates significant advances in cybersecurity AI, we acknowledge several important limitations and safety considerations that inform responsible deployment of the system.
Current Limitations
- Performance may degrade when encountering attack patterns significantly different from training data
- Autonomous response actions require careful configuration to prevent business disruption
- The model requires significant computational resources for real-time operation at enterprise scale
- Integration with legacy security infrastructure may require substantial engineering effort
- Explainability features, while advanced, may not satisfy all regulatory compliance requirements
To address these limitations, we recommend a graduated deployment approach where RakshaAI o3-lla initially operates in advisory mode, with human oversight for all autonomous actions. As organizations build confidence in the system's performance, automation levels can be gradually increased for well-understood threat categories.
We have implemented several safety mechanisms including confidence thresholds for autonomous actions, rollback capabilities for all automated responses, and comprehensive audit logging for all system decisions. These mechanisms ensure that the system can be safely deployed in critical security environments while maintaining operational transparency.
Future Research Directions
Our work with RakshaAI o3-lla opens several promising directions for future research in cybersecurity AI. We are particularly interested in expanding the model's capabilities to handle emerging threat categories and improving its efficiency for deployment in resource-constrained environments.
Current research efforts focus on developing quantum-resistant security analysis capabilities, expanding multimodal threat detection to include voice and video communications, and improving the system's ability to detect and respond to AI-powered attacks. We are also investigating federated learning approaches that would enable continuous model improvement while preserving customer data privacy.
Additionally, we are working to reduce the computational requirements of the model through advanced compression techniques and specialized hardware optimization. These efforts aim to make sophisticated cybersecurity AI accessible to organizations of all sizes, not just those with extensive computational resources.
Investment Opportunity
Conclusion
RakshaAI represents a systematic approach to building breakthrough cybersecurity AI capabilities through progressive model development. Our current o1 model demonstrates strong foundation capabilities with 91.2% threat detection accuracy, while our roadmap to o2 and o3-lla targets transformational advances in autonomous security operations.
The development trajectory from o1 to Agent AI capabilities represents a significant market opportunity in the rapidly growing cybersecurity AI sector. Our systematic approach, proven technical foundations, and clear development milestones position RakshaAI to capture substantial market share as cybersecurity AI becomes essential infrastructure.
Investment in RakshaAI's development roadmap enables breakthrough cybersecurity capabilities that address critical market needs including talent shortages, threat sophistication, and operational efficiency. Our progressive development approach minimizes technical risk while maximizing market opportunity through each model generation.
References
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