AVIZIUMSECURITY SOLUTIONS

EXPERTISE / SPECIALIST SERVICES

AI Application Security

Assess the application. Bound the AI.

Review AI-enabled applications across models, retrieval, APIs, and connected tools. Apply application-security and cloud-architecture discipline to identify exploitable gaps and verify practical fixes across AWS, OpenShift, and hybrid environments.

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Is this the right starting point?

Who this helps

Application and security teams adding AI assistants, retrieval, or agent tools to cloud applications.

Common starting points

A new AI feature, retrieval across customer boundaries, tools with consequential permissions, or concerns about prompt injection and sensitive-data exposure.

What to prepare

The use case, model and hosting providers, data sources, user roles, connected tools, and a general description of expected allowed and denied actions.

01 / ENGAGEMENT SCOPE

Focus on the meaningful gaps.

01

AI application assessments

Map users, data flows, model endpoints, dependencies, and trust boundaries. Review authentication, authorization, output handling, and abuse cases within an agreed testing scope.

02

Prompt injection and unsafe output

Assess direct and indirect prompt injection through user input, retrieved documents, and tool responses. Verify that generated output cannot bypass authorization or trigger unsafe execution.

03

Data protection and retrieval

Review tenant isolation, document permissions, sensitive-data exposure, secrets, retention, and redacted logging. Test denied access and revoked permissions before data reaches the model.

04

Agent and tool permissions

Review least-privilege identities, tool allowlists, execution-time authorization, independent approval for consequential actions, egress boundaries, resource limits, and stop mechanisms.

02 / PRACTICAL OUTPUTS

A clear path from review to action.

Deliverables are tailored to the agreed systems, assessment boundary, and engagement objectives.

  1. 01

    AI application threat model and agreed assessment boundary

  2. 02

    Prioritized findings with reproducible evidence and engineering guidance

  3. 03

    Remediation roadmap, control verification, and retest summary

WORKING TOGETHER

From a clear scope to a practical handoff.

Access, timing, deliverables, and any implementation or retest work are agreed before the engagement begins.

  1. 01

    Agree the boundary

    Define goals, systems, access, permitted work, deliverables, and operational constraints.

  2. 02

    Assess with context

    Review the agreed architecture, controls, and evidence; prioritize actionable gaps.

  3. 03

    Plan accountable changes

    Connect corrective actions to owners, dependencies, due dates, and acceptance criteria.

  4. 04

    Verify and hand over

    Retest agreed changes, summarize remaining decisions, and hand evidence to the maintaining team.

COMMON QUESTIONS

Plan the review
with confidence.

Can prompt injection be solved by a stronger system prompt?

A prompt can guide behavior, but authorization and tool permissions must be enforced by the application and execution environment. A scoped assessment examines those boundaries and their evidence.

Can the assessment cover both retrieval and agent tools?

Yes. Agreed scope can include document access at retrieval time, tenant isolation, output handling, tool identities, execution-time authorization, and approval boundaries.

03 / GET STARTED

Define the scope together.

Describe the AI use case, hosting platform, model providers, retrieval sources, connected tools, and assessment goals. Testing authorization, permitted techniques, data handling, and operational limits are agreed before work begins.

Begin with discovery, agree the scope and deliverables, assess the environment, then review findings and validate agreed changes.

Share only general, unclassified information in the inquiry form. Arrange an appropriate exchange method before sharing sensitive findings or evidence.

Talk to an engineer ↗
01

AI Application Security

Review AI-enabled applications across models, retrieval, APIs, and connected tools. Apply application-security and cloud-architecture discipline to identify exploitable gaps and verify practical fixes across AWS, OpenShift, and hybrid environments.

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02

AWS & Cloud Security Architecture

Review AWS and hybrid cloud architecture across identity, network segmentation, data protection, and operational visibility. Connect design decisions to practical implementation and verification.

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03

OpenShift & Container Security

Review OpenShift and Kubernetes architecture across private and hybrid cloud, from cluster access and workload isolation to AWS integration and verifiable controls.

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04

STIG Assessment & Remediation

Assess applicable Security Technical Implementation Guide requirements, organize supporting evidence, and guide remediation through verification.

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05

Application Security & DevSecOps

Connect cloud application architecture, workload identities, dependency risks, and delivery controls across AWS and OpenShift to a remediation process your engineering team can use.

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AI SECURITY / WATCH & APPLY

New capabilities.
Familiar trust boundaries.

Bring application-security discipline to AI features. Explore prompt injection, retrieval data protection, and agent permissions through an OWASP AI Exchange video and interactive review scenarios.

Explore AI security learning ↗

Explore AI application security assessments ↗

  1. 01

    Watch with a purpose

    An attributed overview paired with questions for your architecture review.

  2. 02

    Test your judgment

    Work through practical AWS, OpenShift, and application security scenarios.

  3. 03

    Apply the controls

    Connect access boundaries, secure delivery, and verification evidence.