The AIAS consortium is proud to announce the completion of Deliverable D3.1 – “AIAS Deception Layer”, a major milestone that advances both the research and implementation of deception-based protection mechanisms for Artificial Intelligence (AI) systems.
This deliverable represents a comprehensive effort combining state-of-the-art research, engineering design, and proof-of-concept implementations to strengthen the resilience of AI-driven infrastructures against emerging cyber and adversarial threats.
A Holistic Deception Strategy for AI Protection
Deliverable D3.1 introduces a multi-layered deception framework specifically tailored to the scenarios defined in Deliverable D2.2. The work focuses on deploying realistic, adaptive, and scenario-aware deception technologies capable of misleading adversaries while simultaneously collecting high-value intelligence.
The AIAS Deception Layer integrates three key technological pillars:
- High-Interaction Honeypots
- Digital Twin Environments
- Virtual Personas
These technologies operate in synergy with a dedicated Monitoring and Security Analytics Platform, enabling continuous data collection, behavioural analysis, and proactive threat detection for AI systems.
Implemented Deception Components and Use Cases
Within D3.1, the consortium designed and implemented multiple targeted deception mechanisms, each mapped to real-world operational scenarios:
High-Interaction Honeypots
- Designed for enterprise IT and IoT environments
- Provide realistic attack surfaces to attract and analyse malicious activity
- Support behavioural profiling of attackers targeting AI-enabled infrastructures
Digital Twin – Robotic Arm
- Simulates industrial robotic systems in high fidelity
- Enables safe observation of attacks against AI-controlled cyber-physical systems
- Supports research on adversarial manipulation of robotic decision pipelines
Digital Twin – Environmental IoT Devices
- Replicates sensor-based environments (e.g., smart buildings, environmental monitoring)
- Enables detection of attacks targeting AI-driven sensing and decision systems
Virtual Persona – Hospital Monitoring Scenario
- Simulates realistic user and system behaviour in healthcare monitoring environments
- Enables safe study of attacks targeting sensitive AI-assisted medical workflows
Monitoring and Smart Security Analytics
Beyond deception deployment, D3.1 delivers a monitoring and security analytics tool capable of:
- Collecting heterogeneous data streams from AI systems and deception assets
- Performing smart analytics for anomaly detection and threat correlation
- Generating actionable intelligence for proactive AI system protection
This monitoring capability ensures that deception is not only defensive but also intelligence-driven, enabling continuous improvement of AI security posture.
Alignment with AIAS Architecture and Requirements
All developed components fully align with the:
- Functional requirements
- Architectural design principles
- User-centred requirements
defined in Deliverable D2.1.
The deliverable provides validated proof-of-concept implementations, demonstrating the technical feasibility, integration capability, and operational value of deception-driven AI protection mechanisms.
Impact within the AIAS Vision
Deliverable D3.1 significantly strengthens the AIAS objective of advancing:
- AI for Cybersecurity
- Cybersecurity for AI
By combining deception engineering with advanced analytics, AIAS moves towards building adaptive, intelligent, and resilient security ecosystems capable of protecting next-generation AI-enabled infrastructures.