AI APPLICATION SECURITY
AI App & Agent Security Review
Assess AI apps, agents, data flows and risky automation before they scale.
Praxis Cyber reviews AI applications, agents and workflows for risks such as prompt injection, sensitive data leakage, excessive access, unsafe tool use, insecure output handling and weak logging.
Prompt injection
Data leakage
Unsafe tool use
Kill switches
Agent risk console
Review mode
Prompt
Model
Tools
Data
Prompt injection
High
Attempts to override instructions or retrieve hidden context.
Sensitive data leakage
High
Private data surfaced through prompts, retrieval or outputs.
Unsafe tool use
Med
Agents taking actions with excessive permissions or weak approval.
Weak monitoring
Med
Limited audit trail, alerts or escalation when AI behaviour drifts.
Before scale: test prompts, data paths, access, outputs and agent actions.
WHY IT MATTERS
Move from AI excitement to controlled adoption.
The most useful AI applications connect to documents, systems and actions. That is also where risk concentrates. Praxis Cyber helps teams understand what could go wrong, prove it with evidence, and implement practical guardrails before workflows become business critical.
Review focus
Prompts, data stores, tool calls, outputs, model settings and monitoring.
Protect sensitive data
Review where private, confidential or regulated information can enter prompts, retrieval, context windows or outputs.
Contain agent actions
Check whether agents and automations can take actions beyond their role, approval model or business risk appetite.
Give governance evidence
Produce practical evidence that supports AI governance, vendor approval, risk acceptance and leadership reporting.
Reduce release friction
Give product, security and business teams a clear path to remediate issues and move forward confidently.
WHAT THIS INCLUDES
A practical review of the AI attack surface.
We focus on real ways an AI application or agent can be misused: hostile prompts, exposed context, over-permissioned tools, weak boundaries, unsafe output handling and missing monitoring.
Prompt injection testing
Test whether prompts, system instructions, memory and hidden context can be overridden or manipulated.
Sensitive data leakage review
Review how private data can enter, persist or leave prompts, retrieval stores, outputs and logs.
RAG and vector store access
Check retrieval design, document boundaries, search access and cross-user context exposure.
Tool and function permissions
Assess tool calling, function scopes, approval paths, service accounts and action boundaries.
Excessive agency checks
Identify risky autonomous behaviours, unsafe action chains and missing human-in-the-loop controls.
Output handling review
Review how AI output is rendered, trusted, executed or passed into downstream systems and users.
Provider and model configuration
Check model settings, provider controls, tenant options, retention choices and security defaults.
Logging and kill-switch checks
Assess auditability, monitoring, escalation, rollback and shutdown options when behaviour changes.
Cost abuse considerations
Review unbounded usage, runaway loops, quota controls and misuse patterns that can drive cost or disruption.
Evidence and remediation guidance
Capture evidence, risk-rank findings and translate issues into practical controls and next steps.
HOW IT WORKS
A review path that mirrors how AI systems actually fail.
We do not stop at a checklist. We map the AI workflow, threat model realistic misuse paths, test the behaviours that matter, then provide the guardrails and monitoring recommendations needed to move forward.
01
Map
Understand models, prompts, users, data flows, connected systems, tools, approvals and access boundaries.
02
Threat model
Identify realistic misuse, failure paths and business impact across people, data, models and automation.
03
Test
Review prompt, data, access, output and agent behaviours with evidence-led testing and control checks.
04
Remediate
Provide guardrails, least-privilege recommendations, monitoring improvements and practical remediation guidance.
Common guardrails
Recommendations are framed so security, product and governance teams can act on them without slowing the entire AI program.
Prompt hardening
Least privilege
Retrieval boundaries
Output validation
Human approval
Runtime monitoring
DELIVERABLES
Evidence your teams can act on.
Each review is designed to make risk visible and remediation practical. Findings are clear enough for leadership, detailed enough for engineers, and structured enough to support AI governance decisions.
AI security review report
A risk-ranked report with tested scenarios, evidence, impact, recommendations, ownership guidance and a roadmap for control uplift.
Typical report sections
Executive summary, architecture notes, attack scenarios, evidence, findings, controls and retest options.
Attack scenarios and evidence
Clear examples of how the AI app or agent could be misused, with enough context for teams to reproduce and understand the issue.
Risk-ranked findings
Prioritised issues based on exploitability, business impact, data sensitivity and control maturity.
Guardrail recommendations
Practical controls across prompts, data access, retrieval, tools, approvals, output handling and model configuration.
Monitoring and escalation
Recommendations for logging, alerting, kill switches, incident paths and accountability when AI behaviour changes.
Remediation guidance
Implementation-focused guidance for product, platform, security and governance stakeholders.
Retest option
Optional retesting to validate that high priority guardrails and fixes are working as intended.
BUYER QUESTIONS
Useful for product, security and governance teams.
Who is this review for?
Teams building or adopting AI-enabled applications, copilots, internal agents, RAG workflows, automations or customer-facing AI features.
Do you need source code?
Source code helps, but the review can also work from architecture, configuration, prompts, access models, sample workflows and controlled testing access.
Can you review agents that call tools?
Yes. Tool and function calling permissions are a core focus, especially where agents can create tickets, query data, trigger workflows or perform privileged actions.
What happens after the review?
You receive risk-ranked findings, evidence and practical remediation guidance. Praxis Cyber can also support uplift, guardrail design and retesting.
Scale AI with confidence, not blind spots.
We help organisations move faster with AI by making the real risks visible and the next controls clear.