Cybersecurity
AI-driven cybersecurity solutions protecting organisations from evolving digital threats.
Our work in Cybersecurity
Create Short Introduction for our work in Cyber Security
Practical AI Cybersecurity Applications
Threat Detection and Response
AI systems continuously monitor network activity to detect unusual patterns or potential attacks. By identifying threats in real time, organisations can respond faster and minimise potential damage.
Phishing and Social Engineering Prevention
Machine learning models analyse communications and user behaviour to detect phishing attempts and other social engineering tactics, helping protect sensitive data and prevent breaches.
Predictive Risk Assessment
AI algorithms scan infrastructure to identify potential vulnerabilities, predict emerging risks, and prioritise mitigation actions. This proactive approach strengthens cybersecurity and reduces the likelihood of breaches.
ADSP and the MoD
Over the past few years, ADSP has successfully delivered several AI projects for the MOD, playing a pivotal role in advancing AI and ML techniques in cyber defence. Our key contributions include:
LLM Agents for Cyber Defence: A Zero-Shot Approach
Task 37: Investigated using LLMs as defence agents to reduce reliance on traditional training methods, demonstrating a 90%-win rate on specific environments.
Task 32: Aims to review the evolving LLM landscape, focusing on reducing latency and expanding memory options to enhance agent performance.
A Generalist RL Agent for Cyber Defence
Task 18: Demonstrated the creation of a single, versatile agent capable of effectively operating across multiple cyber environments.
- Task 40: Created adaptors to streamline RL projects and demonstrate the potential to integrate and analyse various RL models efficiently.
Minimum Viable Product (MVP) Agent Integration
Task 8: Implemented the first integration of a pioneer agent into complex environments, leading to the demonstration of an ML cyber defender outperforming a rules-based agent developed with a human analyst.
- Task 19: Showcased that RL could learn to handle more complex simulation environments, achieving a milestone in addressing the Sim-to-Real challenge.
Decoy Agents: A Generative Approach to Deception
Task 36: Explored the efficacy of using Large Language Models (LLMs) to create realistic decoys to deceive attackers and deflect from intended targets.
Talk-To-Your-Components: Human Programming Interfaces
Task 38: Demonstrated LLMs equipped with retrieval augmented generation (RAG) to interpret complex cybersecurity data into human-readable output.
Probabilistic Graphical Models for Agent Planning
Task 49: Implements a probabilistic graphical model to allow the agent to select actions with a higher likelihood of success, anticipating outcomes.
Data Efficient Reinforcement Learning
Task 17: Proved the ability for the Self-Predictive Representations (SPR) technique to generalise better to unseen tasks more effectively than traditional RL techniques.
Defence Environment Simulations
Task 28: Enabled testing and validation of defence agents in complex cyber environments, enhancing adaptability and effectiveness.
Extending Reinforcement Learning Capabilities
Task 50: Focuses on extending and developing proof-of-concept agent and environment adaptors, enabling other groups to utilise the adaptor functionality.
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