Security and Emerging Technology
AI Security
AI systems introduce failure modes traditional security testing does not look for. We assess and harden them specifically, before they reach production and continuously afterwards.
Overview
Why organisations engage us for ai security
An AI feature typically has broad data access, accepts untrusted natural-language input, and can be persuaded to act outside its intended purpose. That combination creates exposure that a conventional application security review will not surface.
Our AI security work covers the whole path: what the model can read, what it can do, what an attacker can make it do, and what evidence exists afterwards.
Does Acmez provide AI security services?
Yes. Acmez provides AI security assessment, large language model security testing, AI risk assessment, prompt injection protection, AI model protection, secure AI deployment, AI governance and responsible AI compliance support for organisations deploying AI systems.
Engagement models
Fixed scope, dedicated teams, offshore development centre, staff augmentation or managed services.
Compare modelsDelivery locations
Roorkee, Uttarakhand and Bengaluru, Karnataka, serving clients in India and internationally.
Contact our teamWhat is included
What AI Security covers
Each capability below is delivered as part of a wider engagement or on its own, depending on what you need.
AI Security Assessment
End-to-end review of AI system architecture, data flows, tool permissions, trust boundaries and logging.
LLM Security Testing
Adversarial testing for prompt injection, jailbreaks, data exfiltration, tool misuse and unsafe output handling.
AI Risk Assessment
Structured evaluation of accuracy, bias, privacy, safety and operational risk for a given AI use case.
Prompt Injection Protection
Input isolation, instruction hierarchy, output validation, tool permission scoping and confirmation gates for consequential actions.
AI Model Protection
Controls against model and prompt extraction, unauthorised access and abuse of inference endpoints.
Secure AI Deployment
Network isolation, secret handling, rate and cost limits, tenant separation and safe fallback behaviour.
AI Governance
Use-case registry, approval process, human oversight requirements, monitoring and incident procedure.
Responsible AI Compliance
Documentation, transparency notices, data-handling records and evaluation evidence aligned to emerging regulatory expectations.
AI Ethics Review
Assessment of a proposed AI use case against accountability, disclosure, bias, contestability and proportionality before it is built.
What changes
What changes for your organisation
Stated as outcomes we can be held to, without invented figures.
Injection paths closed
Untrusted content is prevented from being treated as instruction, which is the root of most AI compromise.
Blast radius contained
Scoped tool permissions and confirmation gates limit what a manipulated system can actually do.
Sensitive data kept in place
Retrieval boundaries and output filtering stop AI features becoming an exfiltration channel.
Deployment you can defend
Governance records, evaluation evidence and audit logs support internal approval and customer scrutiny.
How we work
How a ai security engagement runs
A consistent sequence, adapted to the size and risk of the work.
Inventory and threat model
Catalogue AI use cases, data access, tool permissions and trust boundaries, then model realistic attacks.
Adversarial testing
Hands-on testing against injection, extraction, misuse and unsafe-output scenarios with documented evidence.
Hardening
Implement input isolation, permission scoping, output validation, confirmation gates and rate limits.
Governance design
Approval workflow, human oversight requirements, retention policy and incident response for AI systems.
Monitoring
Logging of prompts, tool calls and outputs with alerting on anomalous behaviour and cost spikes.
Periodic reassessment
Re-testing as models, prompts, tools and threat techniques change.
Technologies
What we typically build with
Technology is chosen for the problem and for long-term supportability, not from preference. Where your organisation already has a standard, we work to it.
Our engineering standards- OWASP Top 10 for LLM Applications
- NIST AI Risk Management Framework
- Adversarial testing harnesses
- Evaluation frameworks
- Guardrail libraries
- Audit logging
Technology names describe the tools our engineers work with. They do not indicate partnership, certification or endorsement by the respective vendors.
Explore further
Capability that works alongside AI Security
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Related solutions
Where this applies
Banking and Financial Services
Secure, auditable systems engineered for accuracy and regulatory scrutiny.
Healthcare
Clinical and administrative systems built around patient safety, privacy and operational flow.
Government and Public Sector
Citizen services, departmental systems and accessible digital public infrastructure.
Insurance
Policy, claims and document-intensive workflows automated end to end.
Questions & answers
Questions about AI Security
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionPrompt injection is when untrusted content (a web page, an uploaded document, an email) carries text that the AI system treats as an instruction rather than as data. It matters because such a system often has access to internal data and the ability to call tools, so a successful injection can cause data disclosure or unintended actions.
No, and any vendor claiming otherwise should be treated with caution. It is reduced through architecture: separating instructions from untrusted content, scoping tool permissions tightly, validating outputs, and requiring human confirmation for consequential actions. The goal is to make a successful injection low-impact rather than impossible.
Yes. Conventional testing targets code and infrastructure. AI security testing targets model behaviour, instruction handling, retrieval boundaries and tool invocation, which conventional tooling does not examine. We recommend both.
Yes. Responsibility for how the product accesses your data, what permissions it holds and how its outputs are used remains with you. We assess third-party AI deployments as well as systems we build.
They overlap but are not the same. Security asks whether a system can be made to misbehave; ethics asks whether it should exist in that form at all, who is accountable, whether users are told, whether affected people can contest a decision, and whether a simpler tool would serve better. An AI ethics review can be run alongside a security assessment or on its own. Our standing commitments are published on the quality and security page.
Next step
Let us discuss your ai security requirement
Tell us what you are trying to achieve. We will tell you honestly what it takes, including when a smaller engagement would serve you better.