Key Features

Comprehensive security features for your AI applications

Advanced Threat Detection

Real-time protection against prompt injection, data leakage, and malicious inputs.

Regulatory Compliance

Controls and audit evidence mapped to the EU AI Act, the NIST AI Risk Management Framework and ISO/IEC 42001.

Data Privacy

Detects and redacts personal and sensitive data before it reaches a third-party model.

End-to-End Protection

One policy and one audit trail across development, staging and production.

AI Agent Identity Management

Gives each agent its own identity, its own scoped credentials and its own audit trail.

Permission-Based Content Access

Enforces the asking user's permissions on retrieval, so a RAG answer cannot quote a document they cannot open.

What each control actually does

SecureAI Guard sits on the path between your application and the model it calls, so every control below runs server-side and applies to the request on the way out and the completion on the way back. Each section says what is inspected, which published risk it maps to, and what the control does not do — because a security control described only by what it catches is impossible to evaluate.

Advanced Threat Detection

Every request is inspected before it leaves your network: the system prompt, the retrieved documents, the conversation history, the tool definitions and the current user turn. Detection runs across the whole assembled context rather than the user message alone, because the payload in an indirect prompt-injection attack is never in the user message — it is in the wiki page, the support ticket, the PDF or the API response that your retrieval step pulled in on the user's behalf.

Completions are inspected on the way back for the behaviour that indicates a successful attack: an unexpected tool call, a URL assembled out of conversation content, an attempt to emit a system prompt, or an instruction addressed to a downstream component. Detector verdicts are recorded with the context that produced them, so an alert is investigable rather than merely countable.

Where this sits in the published risk taxonomies. Maps to OWASP LLM01:2025 Prompt Injection and LLM04:2025 Data and Model Poisoning.

What it does not do. Detection is not a boundary. There is no reliable way to distinguish an instruction from data inside a single token stream, so a determined attacker with unlimited attempts will eventually get a payload through. What detection buys you is cost, telemetry and the ability to notice. The controls that actually bound the damage are least-privilege tool credentials and human confirmation on irreversible actions.

Example: configuring advanced threat detection

// Initialize SecureAI Guard
const guard = new SecureAIGuard({
  apiKey: 'your-api-key',
  model: 'gpt-4',
  security: {
    threatDetection: true,
    dataPrivacy: true
  }
});

// Protect your AI calls
const response = await guard.protect(
  async () => {
    return await model.generate(prompt);
  }
);

Regulatory Compliance

Compliance work on an AI system is mostly an evidence problem. The frameworks ask you to show which models are in use, what they were allowed to do, what data reached them, how risk was assessed and what happened when a control fired — and almost none of that is recoverable after the fact unless something was recording it at the time. SecureAI Guard produces that record as a by-product of enforcing the controls.

The mappings are to named, dated instruments rather than to a marketing category: the EU AI Act (Regulation (EU) 2024/1689), the NIST AI Risk Management Framework (NIST AI 100-1) together with its Generative AI Profile (NIST AI 600-1, July 2024), and ISO/IEC 42001:2023 for the management-system view.

Where this sits in the published risk taxonomies. Evidence is exported per control so it can be attached directly to an ISO/IEC 42001 Statement of Applicability or a NIST AI RMF Measure/Manage record.

What it does not do. No product makes an organisation compliant with the EU AI Act or any other instrument. Obligations under the Act attach to a provider or deployer of a system, are determined by the system's risk classification, and cover documentation, governance and human oversight that sit well outside any runtime control. SecureAI Guard supplies technical evidence for a subset of those obligations. Read that as help with the paperwork, not as a certificate.

Example: configuring regulatory compliance

// Compliance setup
const complianceManager = new ComplianceManager({
  frameworks: ['EU AI Act', 'NIST AI RMF'],
  monitoring: true
});

// Monitor compliance
complianceManager.monitor();

Data Privacy

The most common data-protection failure in an LLM application is unglamorous: a support agent pastes a customer record into a prompt, and that record is now in a third-party processor's logs in a jurisdiction nobody assessed. SecureAI Guard detects personal and sensitive data in the outbound request — names, national identifiers, payment details, health information, credentials and API keys — and either redacts, tokenises or blocks according to policy.

Tokenisation is the option worth understanding. A detected entity is replaced with a stable placeholder before the call and restored in the completion afterwards, so the model never receives the raw value while the answer still reads correctly. It is the difference between a control that protects data and a control that breaks the product, which is why the redact-everything approach usually gets switched off within a fortnight.

Where this sits in the published risk taxonomies. Maps to OWASP LLM02:2025 Sensitive Information Disclosure. Supports the data-minimisation argument under GDPR Article 5(1)(c).

What it does not do. Entity detection is statistical and it will miss things — unusual identifier formats, personal data expressed as free prose, and anything sensitive only in context. Treat it as a strong reduction in exposure, not as a guarantee that no personal data reaches the provider. If a workload genuinely cannot tolerate that residual, the answer is a deployment model where the prompt never leaves your network, not a better regular expression.

Example: configuring data privacy

// Data anonymization
const privacyGuard = new PrivacyGuard({
  anonymization: true,
  encryption: 'AES-256'
});

// Protect data
privacyGuard.protectData();

End-to-End Protection

Controls that exist only in production are controls that are first exercised in production. The same policy runs in development and in staging, in report-only mode, so a team sees what would have been blocked while there is still time to change the prompt or the retrieval step rather than after a release is cut.

This is also where the model inventory comes from. Enforcing an explicit allow-list of models and providers on every call means the list of what your organisation is actually using is derived from traffic rather than from a spreadsheet somebody maintained for a quarter — and an unreviewed provider appearing in that traffic is both a security finding and a data-transfer finding.

Where this sits in the published risk taxonomies. Supports OWASP LLM03:2025 Supply Chain by making the set of models, providers and versions in use continuously observable.

What it does not do. This covers the inference path — the requests your application makes to a model and the responses it acts on. It does not cover training-data governance, model fine-tuning pipelines or the security of the provider's own infrastructure.

Example: configuring end-to-end protection

// Lifecycle protection
const lifecycleProtector = new LifecycleProtector({
  phases: ['development', 'deployment', 'monitoring']
});

// Implement protection
lifecycleProtector.safeguard();

AI Agent Identity Management

An agent that holds a shared service account inherits the union of every permission that account was ever granted, and its actions are indistinguishable from every other consumer of the same key in the logs. Each agent instead gets an identity of its own, with credentials scoped to the task it is performing and to the user it is performing it for, so a compromised agent can reach only what that one task needed.

Actions that are irreversible or outbound — sending, paying, deleting, publishing, granting access — can be routed to a human confirmation step. This is the single control that most reliably converts a successful prompt injection into a blocked one, because the attacker has to get past a person as well as a model.

Where this sits in the published risk taxonomies. Maps to OWASP LLM06:2025 Excessive Agency.

What it does not do. Scoping tool access constrains what an agent can do; it does not stop the agent being manipulated into doing a permitted thing at an unhelpful moment. Confirmation steps only work while they remain rare enough that people still read them — an agent that asks fifty times a day is training its user to click yes.

Example: configuring ai agent identity management

// Identity management
const identityManager = new IdentityManager({
  agentControl: true,
  accessMonitoring: true
});

// Manage identities
identityManager.controlAccess();

Permission-Based Content Access

A vector store built by indexing everything an organisation owns has, by construction, no access control. Retrieval returns the nearest neighbours by embedding distance, and embedding distance does not know who is asking. This is how a well-meaning internal assistant ends up summarising the compensation review for the person it was discussing, and it is a failure of the retrieval design rather than of the model.

SecureAI Guard filters retrieval by the asking user's entitlements, evaluated at query time against your existing identity provider and document permissions rather than against a copy that was accurate when the index was built. Documents the user cannot open are not retrieved, so they cannot be paraphrased, cited or leaked through a summary.

Where this sits in the published risk taxonomies. Maps to OWASP LLM08:2025 Vector and Embedding Weaknesses.

What it does not do. Filtering at query time is only as good as the permissions your source systems report. If a document is world-readable in the source repository because nobody ever tightened it, retrieval will correctly conclude that the user may read it. Reconciling stale source-side permissions is a prerequisite for retrieval security, not something a guardrail can do for you.

Example: configuring permission-based content access

// Permission-based access
const accessManager = new AccessManager({
  permissions: true,
  userRoles: ['admin', 'user']
});

// Control access
accessManager.enforcePermissions();

Where to go next

How to put SecureAI Guard in front of a model call — the four integration steps, with code.
Explainers — prompt injection, RAG security, agent permissions and the rest of the risk classes these controls address.
Glossary — definitions for the terms used on this page.
Trust and security — how SecureAI Guard handles prompt content, where it runs, and what our compliance position is today.
On-premises deployment — for workloads where prompt content must not leave your network.

Evaluating this yourself

The right way to assess any guardrail product, including this one, is against your own traffic. Run it in report-only mode over a representative week, count what it would have blocked, and read the false positives — a detector that is right in a vendor benchmark and wrong on your prompts is worse than no detector, because it will be switched off during an incident. Tell us what you are building and we will tell you honestly whether this is the right layer for it.