The short answer
AI security services protect AI systems and the data they touch. They cover threats that ordinary application security misses, such as prompt injection, sensitive data exposure through model outputs, insecure tool use by agents, poisoned training or retrieval data and abuse of AI features. iCreativez Technologies assesses AI applications against frameworks such as the OWASP Top 10 for LLM Applications and MITRE ATLAS, tests them through red teaming, and helps your team fix and monitor what we find.
What our AI security services include
Review the architecture, data flows, prompts, tools and access of your AI applications and agents.
Hands-on attacks that try to jailbreak models, extract data, misuse tools or produce harmful output.
Input and output filtering, content isolation for retrieved documents and safe tool-calling patterns.
Stop personal, confidential or regulated data from entering prompts, logs or third-party models without approval.
Least-privilege permissions, approval steps and sandboxing for agents that take actions.
Permission-aware retrieval, source validation and protection against poisoned documents.
Find which AI tools staff are using, with what data, and set practical usage policies.
Logging, abuse detection and response playbooks for AI-specific incidents.
Common AI threats and how we address them
| Threat | What can happen | Typical controls |
|---|---|---|
| Prompt injection | Hidden instructions in user input or documents take over the model | Input isolation, output checks, limited tool permissions |
| Sensitive data disclosure | The model reveals personal or confidential data | Data minimisation, redaction, permission-aware retrieval |
| Excessive agency | An agent takes harmful or unintended actions | Least privilege, approvals, action limits, audit logs |
| Data and model poisoning | Manipulated data changes model or retrieval behaviour | Source validation, access control, monitoring |
| Unbounded consumption | Abuse drives up AI costs or causes outages | Rate limits, quotas, cost alerts |
When to get an AI security review
- Before launch. Any customer-facing chatbot, assistant or agent should be tested before release.
- After adding tools. Each new integration or permission expands what an attacker could make the AI do.
- When handling regulated data. Health, financial or personal data raises the impact of any leak.
- After a model change. Switching models or versions can change how the system responds to attacks.
- For customer or audit requests. Enterprise buyers increasingly ask for evidence of AI security testing.
How we work
- Scope. Agree which AI systems, data and threats are in scope, and the rules of engagement.
- Assess. Review architecture, configuration, prompts and access controls.
- Test. Run red-team and automated tests against realistic attack scenarios.
- Report. Prioritised findings with clear, practical fixes and evidence.
- Fix and retest. Help your team remediate, then retest and set up ongoing monitoring.
Ways to work with us
- Project delivery: an agreed scope, timeline and acceptance criteria, delivered by our team.
- Ongoing team: a small team that keeps building, improving and supporting your AI over time.
- Dedicated engineers who join your team through our IT Staff Augmentation Services, working in your tools and time zone. Typical rates are in our cost and rates guide.
Every engagement starts with an NDA. You own the code, models, prompts and documentation we create, and your data is used only for your project.
Our AI services
- AI development services in Pakistan
- AI automation services
- AI integration services
- Agentic AI implementation
- Generative & agentic AI
- Custom AI development
- AI product development
- AI MVP development
- Enterprise AI solutions
- Enterprise AI & governance
- AI strategy consulting
- AI talent & staff augmentation
See all our AI services in one place.
Ready to talk about AI security?
Tell us your goals and constraints. We’ll reply with a practical approach, timeline and estimate.
Frequently asked questions
What are AI security services?
They assess and protect AI systems, such as chatbots, LLM applications and AI agents, against AI-specific threats like prompt injection, data leakage and misuse of connected tools.
What is AI red teaming?
AI red teaming is controlled, authorised testing in which security specialists try to make an AI system misbehave, leak data or take unsafe actions, so weaknesses can be fixed.
What is prompt injection?
Prompt injection is when instructions hidden in user input, a web page or a document trick an AI model into ignoring its rules or taking unintended actions.
Which frameworks do you use?
We map findings to the OWASP Top 10 for LLM Applications and MITRE ATLAS, and align controls with the NIST AI Risk Management Framework and ISO/IEC 42001 where relevant.
Do you secure AI tools our staff use, like ChatGPT or Copilot?
Yes. We help you find which tools are in use, choose enterprise versions where needed, configure data controls and write practical usage policies.
How often should AI systems be tested?
Before launch, after major changes such as new tools, data sources or models, and on a regular schedule for high-risk systems.