Mental health AI, validated for the moments that matter most.
When a person in distress talks to an AI, the response can change an outcome. SecuraAI provides specialized safety validation for mental-health AI — built on SafeEval, tuned for crisis handling, escalation, and clinical responsibility.
In mental health, an AI's response can be a safety event.
Mental-health AI — support chatbots, screening and triage assistants, companion and coaching agents — increasingly talks to people in vulnerable states. These systems hold deeply sensitive disclosures and, at times, encounter users in crisis.
The bar is unlike any other domain. A system that misses a crisis signal, responds inappropriately, or fails to escalate to a human or appropriate resource is not a quality issue — it is a safety event with real human stakes. Clinicians, regulators, and the public expect these systems to be held to a clinical standard.
Securing mental-health AI means proving — rigorously and repeatedly — that the system recognizes risk, responds safely, escalates appropriately, and protects the person's most sensitive information.
Validated through two completed pilots with mental-health providers.
- Crisis-recognition & safe-response testing
- Escalation & human-handoff validation
- Therapeutic-boundary & sensitive-data review
Where mental-health AI must not fail.
The safety-critical failure modes specialized testing has to cover.
Missed crisis signals
A system that fails to recognize indicators of acute risk — or de-escalates when it should escalate — can leave a person without the help they need.
Escalation & routing failures
When a situation exceeds the AI's scope, it must reliably hand off to a human or surface appropriate resources — every time, not most of the time.
Hallucinated clinical advice
Confident, wrong guidance — an implied diagnosis, or advice to change or stop medication — turns a support tool into a clinical hazard.
Parasocial dependency
Systems that encourage unhealthy reliance — positioning themselves as a person's only confidant — can deepen isolation instead of promoting human connection.
Boundary & identity drift
Drifting outside the intended role — overpromising, role-playing as a clinician, or blurring that the user is talking to an AI rather than a person.
Sensitive-disclosure exposure
Mental-health conversations carry uniquely sensitive data that must be protected from leakage and misuse.
Minor safety
Younger users in distress need age-appropriate handling and protection that holds under sustained pressure.
Adversarial manipulation
Prompt injection and jailbreaks can push a system past the very safeguards that keep vulnerable users safe.
Specialized safety validation, built on SafeEval.
The core engine is SafeEval — multi-turn adversarial safety testing that surfaces failures single-turn checks miss — extended with specialized scenarios for crisis, dependency, boundaries, and minor protection developed for mental-health AI.
- Acute crisis detection & escalation
- Dependency resistance & human-connection promotion
- Boundary maintenance & identity transparency
- Minor protection under pressure
Probe the system for adversarial manipulation — injection and jailbreaks that attempt to bypass safety routing and boundaries under multi-turn pressure.
- Jailbreak & injection resistance
- Safety-bypass attempt coverage
- Multi-turn manipulation testing
Inventory every mental-health AI tool across the organization — including shadow tools — and govern each by sensitivity and risk.
- Find sanctioned and shadow tools
- Risk-tier by sensitivity and exposure
- Continuous governance as tools change
Held to a clinical standard.
Findings structured as evidence for the frameworks that govern mental-health and clinical AI.
Make sure your mental-health AI is safe when it matters.
Start with a safety assessment. We'll validate how your system handles crisis, escalation, and the boundaries that protect vulnerable users.