Risk observation system
A technical map of operational risk in generative AI systems.
The focus is not the model in isolation. It is the point where language connects to data, identity, tools, permissions, and decisions—and where useful capability can acquire operational authority.
Each entry turns that surface into something reviewable: a failure mode, a control boundary, or a governance question supported by evidence.
A reading method for AI systems
For each AI use case, ATLAS maps what the system can do, what it can reach, what constrains it, and who has accepted the remaining risk.
Who has accepted the remaining risk?
Defines what is allowed, who is accountable, what evidence is required, and which risks remain unacceptable.
What constrains it?
Applies identity, permissions, validation, logging, approval, monitoring, sandboxing, and secure implementation.
What can it reach?
Maps data, tools, APIs, repositories, workflows, users, and decisions within reach of the system.
What can it do?
Identifies generation, retrieval, reasoning, transformation, tool invocation, recommendation, and action.
ATLAS reads the stack in both directions: from governance down to implementation, and from capability up to organizational risk.
Enterprise copilots, RAG systems, AI Agents, and connected tools—especially when their output can affect data, access, or action.
Start with a concrete failure mode, trace its exposure path, identify the control boundary, and ask what evidence would make the risk reviewable.
ATLAS links risks such as Shadow AI and Prompt Injection to practical controls, accountable owners, and governance decisions.
Featured research briefing
Why delegating full identity to AI agents is a time bomb, and a five-layer architecture to defuse it before the first incident.
Short technical articles for understanding the exposure before selecting a control.
How Atlas Forge keeps the speed of AI agents while governing an engineering project: defined work, independent verification, and deterministic automation first.
Research Briefing
A methodology for deriving a single canonical risk model from fifteen incompatible security and AI governance frameworks, so a scenario-assessment engine can reason over one fixed vocabulary instead of special-casing every source standard.
A new arXiv paper proposes AgenticAI-Supervisor, a simulation environment that decouples environment creation from scalable execution for verifiable agentic RL.
External intelligence
Recent external signals on AI security, agentic systems and governance.
External signal
arXiv CS.CR
External signal
arXiv CS.CR
External signal
arXiv CS.CR
External signal
arXiv CS.CR
External signal
arXiv CS.CR
External signal
arXiv CS.CR
Failure modes that emerge when language crosses into data, identity, tools, permissions, and decision paths.
Training data, fine-tuning datasets, or embeddings are deliberately manipulated to introduce vulnerabilities, backdoors, or biases that compromise model security and behaviour.
Model-generated output is passed to downstream systems without validation or sanitisation, enabling injection attacks through AI-generated content.
The model generates false or misleading information presented with apparent authority, which users may accept without verification, with legal, reputational, or security consequences.
Operational boundaries that make AI capability observable, constrained, reviewable, and accountable.
Validate inputs to and outputs from AI agent tool calls, plugins, and retrieval pipelines to prevent injection attacks from crossing trust boundaries.
A control pattern that enforces identity-bound authorization and policy decisions outside the agent runtime before tool calls execute.
A maintained record of approved AI tools, allowed use cases, owners, data limits, account requirements, and review status.
Industry standards and regulations that define what evidence, controls, and governance structures AI systems require.
The definitive industry standard identifying the ten most critical security risks for applications powered by large language models, updated for 2026 with refined rankings, new threat categories, and mappings to MITRE ATLAS, CWE, and NIST AI RMF.
A globally recognised knowledge base of adversary tactics, techniques, and case studies for AI systems, modelled after the MITRE ATT&CK framework and maintained by the MITRE Corporation.
A dedicated top-10 list addressing the unique security challenges of agentic AI systems — where LLMs plan, execute multi-step tasks, invoke tools, and operate with delegated authority over enterprise systems.
Use the catalogues to frame a review, then use Frameworks to connect findings to owners and evidence. Learning Log records how the map changes as the field develops.
Secure AI Atlas is built to make AI systems legible before capability becomes authority.
