AI SAFETY & RISK TESTING
Address AI Vulnerabilities Before They Cause Harm
Let Applause security experts manage risk assessments and red teaming for you, helping ensure safety, security and inclusion.
Conventional Testing Methods Won’t Find the Risks That Matter Most
Every AI system has vulnerabilities. Automated tools find them quickly, but threats like prompt injection, bias, data leakage and toxicity require manual review. But at AI’s scale, how can humans cover seemingly infinite edge cases across languages, user contexts and agentic workflows?
Applause combines automation and real-world testing with deep security expertise and documentation to launch safe AI systems confidently. Our AI safety and risk testing services include harm assessment, adversarial prompt testing and expert-led red team engagements, producing findings your team can stand behind.
AI Is Forcing Us to Rethink Digital QA
Despite widespread adoption, it’s still a struggle to maintain AI quality.
of organizations lack human input in the evaluation of AI performance
of Gen AI users have seen content they considered biased
of AI users encountered hallucinations since the start of 2026
of AI users reported misunderstood prompts in that same timeframe
Applause Surfaces Threats Your AI Is Likely to Encounter
Applause fully manages testing in the real world — not a lab. From expert-led harm assessments to adversarial red team engagements, testing scope is matched to your risk profile. General harm assessment covers the broad surface area of bias, toxicity, inaccuracy and privacy exposure. Red teaming engagements go deeper — using adversarial methodology to probe the specific vulnerabilities that matter for your model, application and users.
Applause Tests the Spectrum of AI Risks
We cover six categories of risk, tested by security experts and AI red teaming specialists.
Inaccuracy
Hallucinations, sensitivity to input phrasing, overconfidence and difficulty with fact verification
Bias
Discrimination, social stereotypes, inequitable outputs and conflicts of interest
Toxicity
Hateful speech, obscene language, insults and age-restricted content generated by the model
Privacy
Exposure of sensitive personal information, financial credentials, company code, legal information and more
Misinformation
False or misleading content, including fabricated medical, financial and current events information
Malicious Use
Outputs that assist unsafe behavior, trolling, fraud or other illegal activity
Case Study
Red Teaming and Model Evaluation for a Financial Software Company
A global financial software company partnered with Applause to evaluate and test its Foundational Language Model (FLM) for safety, accuracy and potential harms prior to release. Applause recruited domain experts with financial and credit industry expertise to red-team the model using adversarial techniques across key harm vectors.
- Approximately 10,000 model responses were evaluated for offensive content in 10 days
- 30 CFOs were recruited from the Applause community in one week to test the Financial Analyst AI agent
- The FLM model hallucinated financial products, addresses and personal details, prompting fine-tuning
- Critical issues were resolved across safety, accuracy and domain-specific harms prior to release
How Applause AI Safety and Risk Testing Works
We offer a five-stage process, from risk scoping to documented findings.
1. Risk scoping and system review
We review your AI system's architecture, use cases and known risk areas to build a complete picture of where vulnerabilities may exist, including identifying high-risk domains, defining harm categories and establishing the threat taxonomy for the engagement.
2. Test plan and team development
We design a test plan matched to your risk profile. For general harm assessment, we assemble a diverse team of generalist testers. For adversarial red team engagements, testers are recruited for domain knowledge, security expertise or demographic characteristics relevant to your use case.
3. Testing and adversarial exercises
Testers execute the plan using free-form and guided prompts, covering the full range of harm categories in scope. Red team exercises use adversarial techniques such as prompt injection, role-play exploits, token-level manipulation and jailbreaking. We probe for vulnerabilities that standard testing would not surface.
4. Analysis and risk classification
Every finding is documented, classified by severity and mapped to the harm categories established at the outset. Vulnerability patterns are identified across model behaviors and compiled into a structured risk report.
5. Remediation recommendations
Applause delivers actionable recommendations and can support fix-verify loops to confirm issues are resolved. Findings are delivered in the formats your team already uses, including Jira tickets, GitHub issues or structured risk reports, without adding process overhead. A golden dataset is produced for regression testing.
Comprehensive Results Require an Independent Testing Partner
Organizations need findings from their security assessments to help determine whether their AI is safe to release — and, they need to hold up to engineering, executive and regulatory review. Applause is not a model provider, platform or development tool vendor, so our findings are not influenced by a commercial interest in your model performing well.
Dedicated AI Red Teams (AIRTs) bring structured adversarial expertise to Applause security engagements, stress-testing your systems against known and novel attack vectors. And findings are delivered in the formats your team already uses.
Agentic AI Introduces a New Category of Risk
Agentic systems operate autonomously across multi-step workflows, calling tools and APIs on behalf of your users. A failure can lead to an unauthorized action, a data leak or a policy bypass executed at scale. It’s essential that agents are rigorously tested for security vulnerabilities to prevent these potentially catastrophic outcomes.
With expert-led harm assessments and red teaming, Applause tests the full chain of agent behavior, from prompt intake to tool use to final output. Every engagement considers security and risk testing throughout the agentic development lifecycle and produces annotated traces of tool calls, a failure taxonomy and a fix-verify loop to confirm remediation.
Red Teaming Is Now a Compliance Requirement
Since organizations in high-stakes industries can face severe consequences for non-compliance, AI deployments in finance, healthcare, law and government require more than generalist testing. Applause recruits credentialed specialists — financial analysts, licensed physicians, legal professionals and more — who understand the regulatory obligations and edge cases that define risk in your industry.
U.S. Executive Order
Includes requirements for conducting AI red teaming tests
NIST
Directed to develop evaluation and red teaming guidelines for AI systems
EU AI Act
Requires adversarial testing for high-risk AI models
Ready to Take a Proactive Approach to AI Safety and Risk Testing?
Applause can help you mitigate risks and validate model behavior across real-world conditions, and produce documentation that can help you release AI with confidence. Contact us today to get started.
- Access a global community of 1.5M+ testing experts and end users across 200+ countries and territories
- Find domain experts with credentials in your industry and regulatory environment
- Test for the full range of AI harm categories, including bias, toxicity, inaccuracy, privacy exposure and malicious use
- Receive adversarial findings documented, classified by severity and mapped to remediation actions
- Demonstrate AI safety to engineering, legal and compliance stakeholders with findings that hold up to scrutiny
Dive Deeper Into Digital Quality
From customer stories to expert insights, our Resource Center offers a deeper look at how we approach digital quality.