Make AI security observable,
actionable, and accountable.
AI systems introduce a new security surface across prompts, outputs, models, agents, and the data they interact with. Strai8 continuously monitors AI activity, detects threats and policy violations, and gives security teams the context to investigate, respond, and prove what happened.
AI threats don't wait for a security review.
Potential data exposure
Customer Support Copilot · seen ×3
One alert. The context to understand it. Know what happened, which AI system was involved, what data or systems were affected, who owns it, and what needs to happen next.
Govern, map, measure, and manage AI security.
AI security is not a single control. It is four things done continuously: define what is allowed, understand where the risk actually sits, detect what is happening, and turn what you find into action with an owner and a record.
- Policy violations
- Unauthorised AI usage
- Unapproved models or applications
- Access outside defined boundaries
Define what AI systems are allowed to do, what data they can handle, and what security requirements apply. Detect activity that falls outside those policies.
- Sensitive data exposure
- Connected systems
- AI system dependencies
- Ownership and accountability
Connect AI systems to their users, data, applications, infrastructure and business context, so a security event can be understood in context.
- Prompt injection
- Jailbreak attempts
- Unsafe outputs
- Anomalous behaviour
Monitor AI activity and evaluate behaviour against security, privacy and governance expectations — turning raw AI activity into meaningful security signals.
- Threat severity
- Incident ownership
- Remediation status
- Resolution history
Prioritise incidents by risk, route them to the right owner, track remediation, and keep a record of what happened and how it was resolved.
Five questions security teams can't afford to leave unanswered.
AI security becomes difficult when an incident happens and nobody can quickly establish what happened, what was affected, or who is responsible.
Has anyone attacked our AI systems?
Every meaningful AI security event
Captured and tracked, instead of spread across AI applications and existing security tooling.
Did sensitive or regulated data leave through AI?
What left, and in which interaction
Found here, rather than when a user, a customer or a regulator raises it.
Which AI system was involved?
The AI system, its data and connected resources
AI-specific context arrives with the alert, so nobody reconstructs the scope.
Who owns the system and can fix it?
A named owner for investigation and remediation
Security finds the issue; the person who can act on it is already attached to it.
What do we need to report or disclose?
What happened, what was affected, what was done
A defensible record, assembled while the incident ran rather than afterwards.
Every AI incident arrives with a plan.
Finding a threat is only the beginning. Security needs a clear path from detection to containment, remediation and closure.
Review the AI interaction, affected system, scope and security context before deciding how to respond.
Restrict the affected AI system, access path or behaviour before the issue becomes a larger incident.
Address the policy, configuration, access, model or application issue responsible for the exposure.
Record the resolution, ownership, actions taken and final state, so the incident doesn't disappear into a ticket.
The questions security leaders ask before they trust an AI security program.
AI security introduces a new problem: traditional security controls may tell you that something happened, but not whether it happened inside an AI system, what the AI was doing, or what the business impact was.
Know what your AI is doing before something goes wrong.
Continuously detect AI threats, understand their impact, and give your security team the context to respond before a suspicious interaction becomes an incident.