GenAI Safety: Why AI Generated Content Testing Is a C-Suite Imperative

By: Damien KOPP

Conversational interfaces and AI generated content have become the new interface to engage with customers and internal enterprise users.

It makes their experience more naturally engaging, personalized and interactive.

It brings human traits to technology. It’s both great but also alarming if safeguards are not in place.

Despite its incredible capabilities for searching massive amounts of unstructured data and generating context-aware content, its output accuracy is still somehow questionable and requires rigorous verification.

Where and how this technology should be used certainly matters, as the risk profile varies. But I believe that trust-building human centric design should be at the core of solution design.

I argue here that we should borrow on the learnings from cyber security to approach content safety testing.

It should be continuous, pro-active, and risk based.

And we should assume that malicious actors out there will try everything they can to make your business look bad.

It’s not a matter of IF but WHEN. Hence: you have to be prepared.

In management, the old saying is “trust but verify”.

In this practical guide, I’ll unpack the risk areas to watch for, the regulations to keep an eye on, the approach and tools to test content at scale and most importantly: why organizations must verify before they trust.

I hope you find this useful.

Damien Kopp

Fractional CTO at MISSION+, Founder of KoncentriK

When Content Breaks, So Does Trust

In 2024, it took only one hallucinated refund to trigger a legal precedent.

When Air Canada’s AI chatbot invented a bereavement refund policy, the company refused to honor it — until a judge ruled otherwise. A hallucination wasn’t just a UX bug. It became a contractual obligation.

As enterprises race to deploy GenAI across customer service, healthcare, education, and media, the cost of unsafe outputs is no longer theoretical. It’s legal. It’s financial. It’s reputational. And it’s existential.

But Air Canada is not alone.

AI’s Hall of Shame: Recent Failures, Real Impact

From hallucinated legal filings to bots advising minors on hiding alcohol, the GenAI safety record is under increasing scrutiny. Consider:

• xAI Grok denied the Holocaust, amplifying conspiracy theories due to a rogue prompt edit.
• Anthropic’s Claude hallucinated legal citations, leading to court embarrassment.
• Meta’s AI personas engaged minors in sexually explicit conversations.
• Character.AI faced lawsuits for chatbots promoting self-harm to teens.
• DeepSeek’s model failed 50 safety prompts.

This is just a sample. From Snapchat to Samsung to Google Bard, the incidents are not just PR nightmares. They are warnings.

What these failures make clear is that deploying GenAI without guardrails is no longer an option. That’s where structured, proven frameworks become essential.

At MISSION+, we guide AI projects from idea to outcome using the AI Adoption Pyramid, which offers a reliable blueprint for building in security, compliance, and responsible AI from the ground up.

With foundational layers focused on compliance, data privacy, and security, the Pyramid helps enterprises avoid the pitfalls of rushed experimentation and move toward scalable and trustworthy solutions.

The 14 Content Safety Risk Categories

To make sense of these failures, our framework identifies 14 distinct risk categories:

• Brand Values Misalignment: Content contradicts DEI or ethical principles. e.g., insensitive tone during crisis comms.
• Brand Identity Deviation: Style inconsistency hurts brand equity. e.g., emojis in a luxury finance chatbot.
• Hallucination Risk: Fabricated facts or citations. e.g., fake case law submitted in legal filings.
• Inaccuracy Risk: Misreading correct data. e.g., incorrect dosage from a healthcare assistant.
• Harmful Content Risk: Offensive, unsafe outputs. e.g., promoting eating disorders.
• User Experience Risk: Frustration from ambiguity or latency. e.g., AI contradicts itself mid-chat.
• Data Privacy & Security Risk: PII leakage, prompt injection. e.g., leaking chat history across users.
• Bias & Fairness Risk: Disparities across demographic outputs. e.g., unequal image representation.
• Legal & Compliance Risk: Violations of GDPR, HIPAA, PDPA. e.g., unconsented data use.
• Operational Continuity Risk: Fallback or uptime failures. e.g., no escalation path during failure.
• Agentic Behavior Risk: Rogue tool use or goal drift. e.g., persistent memory violating reset policy.
• Identity Spoofing Risk: Unauthorized voice/persona mimicry. e.g., bot mimics CEO or political figure.
• Tool Misuse Risk: Unsafe use of APIs/functions. e.g., triggering financial transactions erroneously.
• Monitoring & Auditability Risk: Inability to trace behavior. e.g., no log for compliance review.

The Regulatory Landscape: Fragmented but Unforgiving

Regulators are catching up, and staying on top of them all is not easy. Here’s a brief mapping:

• EU: AI Act, GDPR: Mandatory documentation, risk classification, and user rights.
• US: Federal Agency AI Use, NIST AI RMF: Promotes red-teaming, watermarking, model disclosures.
• UK: ICO Guidance on AI: Guidance on fairness, DPIAs, explainability.
• China: Generative AI Measures: Requires content filtering, real-name registration.
• Singapore: PDPA, AI Verify: Voluntary testing toolkit for robustness, fairness, and safety.
• Global: OECD AI Principles, ISO/IEC 42001: International norms and lifecycle compliance.

How to Test: Practical Techniques for Content Safety

You don’t need to guess if your AI is safe. You can test it. Here’s how:

• Red Teaming: Run adversarial prompts using Promptfoo and OWASP GenAI Guide to stress test model defenses.
• Groundedness Testing: Verify factual claims with RAGAS and DeepEval to ensure outputs align with source data.
• Bias Audits: Use Aequitas and Fairlearn for demographic analysis to detect and improve fairness in AI.
• Voice & Persona Control: Detect impersonation via Podonos and Google ASR Logging to prevent misuse and identity spoofing.
• Explainability: Make model decisions auditable using SHAP, LIME, and Alethea to build transparency and trust.
• Privacy & Injection Testing: Guard against prompt injection with AIRIA and OWASP LLM Top 10 to avoid data leaks and attacks.
• Monitoring Readiness: Deploy LangSmith and Guardrails AI for production observability to track performance and failures in real-time.
• Regulatory Simulation: Run audit simulations with AI Verify and IBM AI FactSheets to prepare for compliance and governance reviews.

Final Word: Don’t Trust — Test

Generative AI is no longer a novelty. AI generated content is becoming the new user interface. But with that power comes risk.

The question isn’t “Can your AI chat?” It’s “Can your AI be trusted to represent your brand, comply with law, and avoid harm — at scale?”

At MISSION+, we look at GenAI Safety and Assurance to answer exactly that. We help enterprises test before they deploy — and monitor continuously after.

If it speaks for your company, it must be safe.

Contact us to explore how your GenAI performs under pressure. Let’s test it — together.