AI security research
Research and analysis on AI agent security, autonomous AI governance, and emerging threats in the AI infrastructure landscape.
Featured research
The case for AI agent identity
Why autonomous AI systems require cryptographically verifiable identity, and what happens when organisations deploy agents without it. An analysis of identity gaps in current enterprise AI deployments.
Agent identityWhy AI agents should not have permanent trust
Traditional security models grant access once and verify rarely. AI agents operate continuously, make autonomous decisions, and evolve over time. This paper proposes a continuous trust evaluation model with seven discrete trust states.
Trust modelsThe attribution problem in autonomous AI
When an AI agent causes harm, who is responsible? Without immutable attribution, organisations cannot answer that question — and regulators will not accept silence.
AI governanceResearch categories
Agent identity
Cryptographic binding, origin verification, and persistent identity across AI agent lifecycles.
Explore agent identity →Trust models
Continuous evaluation, behavioural baselines, and dynamic trust state transitions.
Explore trust models →AI governance
Policy enforcement, human oversight, and regulatory alignment for autonomous systems.
Explore AI governance →Threat analysis
Emerging attack vectors specific to AI agents, including impersonation, prompt injection, and agent hijacking.
Explore FloodGate →MSP security
Practical security assessment, evidence collection, and compliance for managed service providers.
Explore Stratum →Stay informed
Receive updates on our research, product development, and insights into the AI security landscape.