Home/AI Security
Secure AI, Portals &
Business Systems.
Security assessments, API hardening, and AI system reviews for businesses deploying AI agents, B2B portals, and private language models — before a vulnerability becomes an incident.
The Problem
AI Systems Introduce Risks That Traditional Security Tools Don't Cover.
A well-secured server running a poorly secured AI agent is still a vulnerable system. The model and agent layer requires its own review.
AI access risks
AI agents granted broad tool access can read, write, or send data they should not. Most deployments are not scoped to least privilege.
Data leakage
Models trained on or given access to sensitive data can reproduce that data in responses — through normal operation, not a breach.
API exposure
APIs that serve AI systems and portals often lack proper authentication, rate limiting, or monitoring — making them the easiest path in.
Permission issues
Role-based access control is misconfigured or absent — users see data outside their scope, agents operate with admin-level permissions they do not need.
Portal vulnerabilities
B2B portals expose internal systems to external partners. Unauthorised access, session hijacking, and insecure direct object references are common.
The Solution
Find the Vulnerabilities Before Your Customers Do.
Polemica reviews the full stack of AI-integrated systems — the model and agent layer, the APIs they call, the portals they power, and the infrastructure they run on. Each review produces a prioritised findings report with specific, actionable remediation steps. We fix what we find.
We do not sell compliance theatre. If a finding is low risk in your specific context, we say so. If a vulnerability is critical and needs to be fixed before you go live, we say that too — and we stay involved until it is resolved.
Reduce risk before deployment
Finding a prompt injection vulnerability in a security review is far cheaper than finding it after a customer exploits it.
Protect company and customer data
Prevent data leakage through misconfigured AI systems, exposed APIs, and poorly scoped agent permissions.
Deploy AI systems with confidence
Ship AI agents and portals knowing the access controls, data handling, and failure modes have been reviewed.
Meet customer and partner requirements
Larger clients and regulated industries often require security reviews before connecting to your systems.
Services
Five Security Reviews.
AI Security Review
Audit your AI agents and workflows for prompt injection, data leakage, and access control vulnerabilities.
- Prompt injection attack vectors
- Tool and API permission scope
- Output validation and filtering
- Sensitive data exposure in responses
API Security Review
Harden the APIs your AI systems and portals rely on — authentication, rate limiting, input validation, and monitoring.
- Authentication and token security
- Rate limiting and throttling
- Input validation and sanitisation
- Logging and anomaly detection
Portal Security Review
Secure your B2B portals against unauthorised access, data exposure, and session vulnerabilities.
- Access control and session management
- Insecure direct object references
- Data isolation between partner accounts
- Audit trail completeness
Private AI Security Review
Review the security posture of your private LLM deployment — data isolation, access control, and model safety.
- Infrastructure and network isolation
- Data ingestion pipeline security
- Model access and authentication
- Output safety and filtering
MCP Security Review
Audit Model Context Protocol server implementations for tool misuse, permission escalation, and injection risks.
- Tool permission scope and least privilege
- Prompt injection via tool responses
- Server authentication and transport security
- Tool call logging and monitoring
Topics Covered
Every Review Covers These Areas.
Each security area is reviewed as it applies to your specific deployment — not as a generic checklist.
Authentication
Verifying that users and systems are who they claim to be — API keys, OAuth tokens, and session management.
Authorization
Controlling what authenticated users and agents are permitted to do — enforcing access rules at every layer.
Role-Based Access
Scoping permissions to the minimum required for each role — staff, partners, agents, and administrators.
Data Isolation
Ensuring that data belonging to one user, tenant, or partner cannot be accessed or inferred by another.
Audit Logs
Recording who accessed what, when, and what actions were taken — for incident response and compliance.
Encryption
Data encrypted in transit and at rest — with key management practices that match the sensitivity of the data.
Agent Permissions
Limiting what AI agents can read, write, and call — preventing agents from taking actions outside their intended scope.
Comparisons
How It Compares
AI Security vs Traditional Cybersecurity
AI Security
- Reviews model and agent layer
- Addresses prompt injection
- Audits tool and API permissions
- Assesses data exposure via outputs
- Specific to AI deployment risks
Traditional Cybersecurity
- Reviews network and application layer
- Does not cover prompt injection
- Covers network access controls
- Does not assess AI output risks
- Does not cover agent autonomy risks
AI security addresses the risks above the infrastructure layer. Both are needed for a complete posture.
Private AI vs Public AI
Private AI
- Data stays on your infrastructure
- Model access fully controlled
- No third-party data processing
- Audit trail under your control
- Offline capable
Public AI (ChatGPT etc)
- Data processed on provider servers
- Provider controls model access
- Terms allow data use for training
- Audit trail at provider discretion
- Requires internet connection
Private AI eliminates the data custody risk. It still requires its own security review — infrastructure security is your responsibility.
Secure APIs vs Open APIs
Secured API
- Authenticated — every caller verified
- Rate limited — abuse prevented
- Input validated — injections blocked
- Monitored — anomalies flagged
- Scoped — minimal data returned
Unsecured API
- Unauthenticated or weak auth
- No rate limiting
- Unvalidated inputs accepted
- No monitoring
- Returns full data objects
An open API connected to an AI system or partner portal is among the highest-risk exposures in most deployments.
Industries
Sectors Where AI Security is Non-Negotiable.
Manufacturing
Supplier portals, inventory systems, and operational AI exposed to external partners require strict access control and audit logging.
Construction
Project portals and AI tools handling subcontractor data, specs, and financials need data isolation and role-based access.
Healthcare
AI systems handling clinical documentation, patient queries, or administrative data require the highest standards of data isolation and access control.
Professional Services
AI tools with access to client files, matter data, and confidential communications need output filtering and permission scoping.
Common Questions
What People Ask Before They Start.
20 questions covering AI security risks, how reviews work, what we find, and when a review is — and is not — sufficient.
AI security is the practice of identifying and mitigating risks specific to AI systems — including prompt injection, model data extraction, insecure tool and API connections, agent permission escalation, and sensitive data exposure through model outputs. It covers the model and agent layer on top of traditional network and application security.
Security Review
Know Your AI Is Secure.
Book a security review. We assess your AI systems, identify the highest-risk exposure points, and fix them before they cause a problem.