AI Security & Governance
Security and accountability for AI that acts.
QNE Labs helps organizations understand the risks introduced by AI systems and agents, define the controls they need, and build the evidence to show those controls work.
01
AI runtime security
Most AI risk materializes at runtime — when a model receives real input, retrieves real data and takes real actions. Security has to operate at that moment, not only at design review.
- Observe prompts, retrieved content, responses and tool calls as they occur
- Treat agents as privileged actors with identities, permissions and limits
- Contain the blast radius of a single manipulated instruction
02
Prompt-injection threats
Prompt injection occurs when content an AI system processes carries instructions that override its intended behavior. Indirect injection — through documents, web pages, emails or tool outputs — is especially hard to see.
- Treat all retrieved and third-party content as untrusted input
- Separate instructions from data and constrain follow-on actions
- Inspect interactions for injection signals and record what was found
03
Sensitive-data protection
Confidential, personal and regulated data can leave through prompts sent to model providers, through outputs and through the tools agents call. Data protection practice must extend to every one of those paths.
- Detect sensitive data before it reaches a model or leaves the system
- Enforce user permissions at retrieval time, not only at the application layer
- Minimize what is logged and retained
04
Policy evaluation
Security policies for AI must be explicit enough to evaluate consistently. Each interaction should be judged against policy and produce a decision that can be explained later.
- Express what agents may and may not do as evaluable policy
- Produce explicit outcomes — for example allow, flag for review or block
- Validate enforcement before relying on it in production
05
Governance and audit evidence
Governance depends on evidence. Organizations need a durable record of what an AI system saw, what policy decided and what happened next.
- Record security events with enough context to reconstruct decisions
- Protect the integrity of AI security records and control access to them
- Map controls to the governance frameworks your organization uses
Security assessment services
An AI security assessment, scoped to your environment.
Assessments are tailored to each organization. Scope, timing and deliverables are agreed in writing before work begins.
Typical areas of review
- Inventory of AI systems, agents, models and providers
- Agent identities, permissions and tool access
- Exposure to direct and indirect prompt injection
- Sensitive-data flows through prompts, retrieval, outputs and logs
- Runtime monitoring, policy and evidence capture
- Governance alignment, for example with the NIST AI Risk Management Framework
How an engagement works
- 01Discovery callUnderstand your AI systems, goals and constraints.
- 02ScopingAgree objectives, systems in scope and deliverables in writing.
- 03ReviewExamine architecture, configurations and workflows with your team.
- 04FindingsReceive prioritized findings and practical recommendations.
Assessments provide expert review and recommendations. They are not a certification, audit opinion or guarantee of security. Pricing is provided on request after scoping.
Start with the AI Agent Security Checklist.
A practical self-review across permissions, prompt injection, sensitive data, runtime policy and audit evidence.