AI, Data & Intelligence
AI Red Teaming
Adversarial exercises in which specialists try to make your AI system misbehave, including jailbreaks, harmful or biased outputs, data leakage and misuse of tools, before real users or attackers do.
Capability overview
What ai red teaming involves
Standard testing checks that an AI system does what it should. Red teaming checks what else it can be made to do. Testers adopt the mindset of malicious users, curious customers and careless employees to find jailbreaks, offensive or dangerous content, leaks of confidential data and ways to abuse connected tools.
Exercises combine automated attack generation, using open tools such as PyRIT or garak across thousands of variations, with creative manual testing by people who understand your domain. Findings are classified by severity and harm type, and the attack prompts become part of your ongoing regression suite.
What is included
What red teaming tests
Jailbreak resistance
Role-play, encoding, multi-turn manipulation and other techniques used to bypass safety instructions and content policies.
Harmful and off-brand content
Attempts to produce offensive, dangerous, defamatory or legally risky statements attributed to your organisation.
Bias and stereotyping
Probing responses across names, genders, regions, castes, religions and languages for unequal or stereotyped treatment.
Confidential data leakage
Efforts to extract system prompts, other users' data, internal documents or credentials.
Tool and agent abuse
Scenarios that trick agents into unauthorised actions, such as sending data externally or changing records.
How we work
How we deliver ai red teaming
Harm scoping
The harms that matter most for your context agreed with product, legal and risk teams.
Test plan and personas
Attacker personas and scenario lists prepared, including domain-specific misuse cases.
Automated campaigns
Large-scale generated attacks run and scored to find weak areas quickly.
Manual exploration
Experienced testers pursue promising weaknesses with creative multi-turn attacks.
Severity reporting
Reproducible findings rated by impact and likelihood, with mitigations and retest after fixes.
Related capabilities
Related capabilities in Responsible AI & AI Security
Prompt Injection Risk Assessment
A focused assessment of how exposed your RAG applications, copilots and AI agents are to direct and indirect prompt injection, and what an attacker could achieve through it.
AI Privacy & Data Protection
Privacy by design for AI systems: lawful data use for training and inference, DPDP Act and GDPR obligations, data minimisation, retention, vendor terms and handling of individuals' rights when AI is involved.
Bias & Fairness Assessment
Measurement of whether an AI system produces unequal outcomes or error rates for different groups of people, with statistical analysis, root cause investigation and practical mitigation options.
Model Explainability
Techniques and interfaces that show why an AI model produced a particular prediction or decision, for data scientists debugging models, staff reviewing cases, regulators and the customers affected.
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Where this applies
Healthcare & Life Sciences
Technology systems for regulated environments where privacy, auditability and continuity…
Manufacturing & Industrial
Connected operations, asset, field, supply chain and industrial platforms for complex operating…
Banking, Financial Services & Insurance
Technology systems for regulated environments where privacy, auditability and continuity…
E-Commerce
Digital platforms for customer experience, operations, commerce, content, marketing and service…
Questions & answers
Questions about AI Red Teaming
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionBefore public launch, after major model or prompt changes, and periodically for high-exposure systems. Early red teaming during beta is cheaper than responding to a public incident.
Every language model application has some weaknesses. The report shows severity and ease of exploitation so you can decide which must be fixed before launch and which are acceptable with monitoring.
Yes. Attacks and harm checks can be run in Hindi, code-mixed text and other languages your users speak, since safety behaviour often differs from English.
Each exercise is fixed price based on the number of harm categories, languages and testing days, with a retest of fixed issues included.
Next step
Discuss ai red teaming with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.