//AI AGENT SECURITY

Healthcare AI Agents Are Operating Without a Safety Net

AI agents in healthcare — clinical decision support, patient triage, care coordination, administrative automation — are accessing patient data, making recommendations, and taking actions autonomously. Most organisations have no systematic way to assess whether those agents are secure.

Periculo specialises in AI agent security for healthcare. We assess the attack surface, identify the risks specific to your deployment, and give you the evidence you need to proceed with confidence.

Why Healthcare AI Agents Are Uniquely High-Risk

Three factors combine to make AI agents in healthcare significantly more dangerous than AI in other sectors.

Patient Data at the Centre

Healthcare AI agents operate with continuous access to sensitive patient information — medical histories, diagnoses, medications, and personal data. A compromised agent doesn't just leak a document; it has ongoing access to some of the most sensitive data that exists. EU AI Act classifies this as high-risk under Annex III. So does MHRA for AI as a Medical Device.

Clinical Decisions with Real Consequences

When an AI agent recommends a treatment pathway, flags a deteriorating patient, or routes a clinical alert — errors have patient safety implications. Prompt injection attacks, model manipulation, or data poisoning don't just cause system failures. They cause clinical failures. DCB0129 and DCB0160 require evidence that these risks have been assessed.

Agentic Autonomy Amplifies Everything

Unlike traditional software, AI agents take actions — they call APIs, write records, send notifications, and spawn sub-agents. The OWASP LLM Top 10 identifies excessive agency (LLM08) as one of the highest-risk vulnerabilities in AI systems. When an agent can act without human review, the blast radius of a security failure grows significantly.

The Attack Surface Is Novel

Prompt injection (OWASP LLM01, MITRE ATLAS AML.T0051) is the most underestimated risk in healthcare AI deployment. Malicious instructions can be embedded in patient records, clinical documents, or external data sources — and executed by your AI agent without any human ever seeing them. Traditional penetration testing doesn't catch this.

PROMPT INJECTION

TOOL & API SECURITY

DATA GOVERNANCE

SUPPLY CHAIN

Prompt Injection & Input Manipulation

We test whether your AI agent can be manipulated through malicious inputs — whether from users, external data sources, or other agents. This includes direct prompt injection (OWASP LLM01), indirect injection through documents and data, and jailbreak resistance.

Every pathway through which external content reaches your agent is tested. In healthcare, indirect injection via clinical notes, discharge summaries, and lab reports is the highest-risk variant — and the hardest to detect with traditional controls.

OWASP LLM01 and MITRE ATLAS AML.T0051 both classify prompt injection as a primary attack vector. Our testing methodology is designed specifically for healthcare AI deployment contexts.

Tool Use & External API Security

AI agents with tool access — to databases, APIs, EHR systems, communication platforms — have an expanded attack surface. We assess whether tool permissions follow least privilege (OWASP LLM08) and whether tool outputs are validated before use.

We test whether agents can be manipulated into misusing their tool access through crafted inputs or prompt injection. Composio connectors, webhook integrations, and MCP servers are all in scope.

In practice, agents are routinely granted far more tool access than their intended workflow requires. Least-privilege is foundational in security; in agentic AI, it is almost never applied by default.

Data Flows & Patient Data Governance

We map every pathway through which patient data touches your AI system — including third-party providers such as LLM APIs, tracing tools, and error tracking platforms.

LangSmith, Sentry, Portkey and similar tools may capture patient data in traces and logs. We identify these flows, assess the governance implications under GDPR and DSPT, and recommend controls.

For NHS-connected deployments, we produce data flow documentation in the format required for DPIA submission and DSPT evidence — not just an internal security report.

AI Supply Chain Security

Your AI agent depends on LLM providers, embedding models, vector databases, and third-party tools. We assess the security posture of your AI supply chain — model provenance, dependency risks, and API key management.

We review sub-processor data processing agreements and assess whether third-party components introduce regulatory risk under GDPR, EU AI Act, or MHRA requirements.

MITRE ATLAS includes supply chain compromise (AML.T0010) as a primary attack vector against AI systems. In healthcare, a backdoored model component could systematically manipulate clinical outputs in ways that are near-impossible to detect until harm has occurred.

Why Choose Our Approach?

HEALTHCARE SPECIALISTS

We understand clinical workflows, NHS procurement, and the regulatory context your AI operates in. Not generalist security — specialist healthcare AI.

OWASP & MITRE ATLAS

Every finding is mapped to recognised frameworks — OWASP LLM Top 10 and MITRE ATLAS adversarial ML techniques. Clear, auditable, defensible.

DTAC & MHRA READY

Our reports produce structured evidence that maps to DTAC requirements and supports MHRA AIaMD engagement. Built for NHS procurement.

ASSURED BY PERICULO

Qualifying deployments receive the 'Assured by Periculo' certificate — independent, third-party validation you can use with commissioners and investors.

BOOK A 15-MINUTE
CALL

Tell us about your AI agents, what data they access, and your deployment context. No pitch, no obligation.

WE SCOPE THE
ASSESSMENT

Within 48 hours we'll send you a clear scope covering your agent architecture, tool surface, and data flows — with an indicative cost.

WE TEST YOUR
AI AGENTS

Prompt injection testing, tool use review, data governance assessment, and supply chain analysis — all mapped to OWASP LLM and MITRE ATLAS.

YOU RECEIVE THE
REPORT

Findings graded by severity, mapped to OWASP LLM IDs. Remediation guidance and 'Assured by Periculo' certificate for qualifying deployments.

Frequently Asked Questions

How is AI agent security different from traditional pen testing?

AI agent security addresses risks unique to autonomous, tool-using AI systems — prompt injection, excessive agency, multi-agent trust failures, and AI supply chain compromise. Traditional penetration testing covers network, application, and infrastructure vulnerabilities but doesn't test LLM-specific attack surfaces. You need both.

Who needs an AI agent security assessment?

What does a Periculo AI agent security assessment cover?

How long does an assessment take?