The Hidden Risks of AI-Powered Web Search for Businesses

AI Search Is Everywhere — But Is It Safe?

Over half of us are now relying on the AI to search the web; however, the accuracy of the AI-generated outcomes remains a concern. According to the recent searches, a common generative AI tool is more often producing misleading or incorrect information; a risk that is extended beyond casual browsing and also directly affects business-critical decisions. 

As there are companies that adopt these tools for compliance, legal guidance, financial research and strategy development, the stakes are now rising. Errors from AI search engines can lead to reputational damage, compliance failures, and poor decision making. In other words — when businesses lean on AI for answers, they may be placing too much trust in imperfect tools.

In this article, we break down the core risks associated with AI-powered web search, explore why they matter for businesses, and offer actionable strategies to mitigate them.

Understanding the Accuracy Gap in AI Web Search

What the Investigation Found

A recent survey and investigation tested six major AI web search tools — ChatGPT, Google Gemini (standard and AI Overview mode), Microsoft Copilot, Meta AI, and Perplexity — across 40 business-relevant questions in areas like finance, law, and consumer rights.

Here’s what emerged:

• Perplexity scored the highest in factual accuracy (~71%)
  
• Google Gemini AI Overviews followed closely (~70%)
  
• ChatGPT, despite its popularity, scored only ~64%
  
• Meta AI ranked lowest at ~55%
  

These results highlight a troubling discrepancy: just because an AI tool is widely used doesn’t mean it’s the most reliable for business insights.

Real-World Implications of Mistakes

Some of the errors uncovered during the test are anything but trivial:

• Financial Risk: When prompted about how to invest a £25,000 ISA (Individual Savings Account) allowance, both ChatGPT and Copilot failed to flag a deliberately mis-stated statutory limit — potentially leading to regulatory risk.
  
• Incomplete or Misleading Advice: Even high-scoring tools sometimes gave partial or superficial answers, glossing over regulatory complexity or failing to recommend critical disclaimers.
  

For executive teams, legal departments, and financial officers, these kinds of missteps are not just inconvenient — they’re dangerous.

Why Does AI Web Search Get It Wrong?

To understand the risk, it’s important to examine the root causes. Here are some of the major technical and systemic issues.

1. Hallucinations

One of the most well-known risks with generative AI is hallucination — when the model confidently draws on spurious or fabricated information. These errors are particularly dangerous in business settings, where decisions hinge upon precision.

2. Malicious or Unsafe Content

Emerging research shows that some AI-powered search engines (AIPSEs) may inadvertently cite harmful URLs or link to phishing sites when responding to benign queries.

Such vulnerabilities have been demonstrated in studies: unfiltered retrieval mechanisms can pull in malicious content that the LLM then repeats.

3. Prompt Injection & Data Manipulation

AI systems are vulnerable to prompt injection, where hidden or malicious instructions are embedded in queries or documents.

This can change the AI’s behavior in dangerous ways — especially when combined with “jailbreak” attacks.

4. Opaque Source Attribution

Unlike traditional search engines that clearly show the source of each result, many AI tools aggregate and summarize information without transparent citations. This “black-box” compilation makes it difficult for business users to verify the credibility or recency of the underlying data.

Without visibility into where information comes from, errors may be hard to trace or correct.

5. Data Governance and Shadow AI

Many organizations don’t fully control how employees use AI tools (“shadow AI”), increasing exposure to data risks.

When AI is used without oversight, sensitive corporate data may be fed into public LLMs, potentially exposing proprietary or regulated information.

6. Model Poisoning & Data Contamination

If an attacker can introduce manipulated or malicious content into the data pipeline (e.g., RAG vector stores or public web sources), the AI may generate dangerously skewed responses.

These attacks are especially insidious because they exploit the system’s reliance on external data.

Why These Risks Matter for Business

Compliance and Legal Exposure

When businesses use AI for regulatory research or legal advice, inaccurate or incomplete outputs can lead to non-compliance, fines, or litigation. The financial-planning example above (ISA allowance mistake) illustrates exactly how this can go wrong.

Reputational Damage

If AI tools misrepresent your company (e.g., citing false statements about your business), correcting those errors can be difficult. There’s no “edit my company’s AI summary” button — and the damage may spread quickly if trusted AI channels repeat the mistake.

Business Decision Risk

Executives may make high-stakes decisions based on AI-generated research. If that research is flawed, the decisions can be flawed too — ranging from financial forecasting to strategy planning.

Trust & Adoption Risk

The internal stakeholders may also begin to not trust the AI if there are errors that frequently occur or have a high impact.  This impacts the adoption and also hampers the value of the company that it has hoped to unlock with the help of these generative tools.

Cybersecurity and Data Leakage

Mismanagement or ill use of the AI can lead to results like data leaks, extracted confidential data or even compliance violations (eg, GDPR) if there is any type of sensitive information shared with unsafe or untrusted AI systems.

Mitigation Strategies: How Businesses Can Protect Themselves

Recognizing these risks is only the first step. Here are practical strategies companies can adopt to manage and reduce AI web search risk.

1. Adopt Human-in-the-Loop (HITL)

• Anytime AI is used for important business research — finance, legal, strategy — requires human review of outputs.
  
• Use internal subject-matter experts to validate critical AI-generated information before action is taken.
  
• Incorporate a feedback loop so that you track where AI was wrong and learn from it.
  

The investigation that exposed the accuracy gap also recommended mandatory “human-in-the-loop” processes for business-critical use.

2. Limit Use to Trusted Tools & Models

• Vet AI tools before adoption: look not just at popularity, but at real-world factuality, safety measures, and transparency.
  
• Favor models that support Retrieval-Augmented Generation (RAG), as these have better grounding in external data.
  
• Choose providers that offer explainability, source attribution, and option to link back to original materials.
  

3. Implement Governance Policies

• Develop an AI governance framework covering acceptable use, data classification, and risk thresholds.
  
• Create “AI usage guidelines” to prevent misuse (especially shadow AI): who can use which tools, for what purpose, under what oversight.
  
• Incorporate AI risk assessment in vendor and procurement policies.
  

4. Monitor and Audit AI Outputs

• Regularly audit AI-generated content to identify misrepresentations or inaccuracies.
  
• Track common error patterns: hallucinations, out-of-date references, or non-authoritative sources.
  
• Use internal or third-party tools to flag risky or malicious AI content.
  

5. Secure AI Pipelines

• For RAG systems: sanitize your retrieval data, validate content before adding to vector stores, and enforce access controls.
  
• Use URL-detection or filtering models to detect malicious or suspicious domains. Research has shown that combining retrieval models with URL detectors helps defend against malicious-citation risk.
  
• Harden AI prompts and system architecture against prompt-injection attacks. Adopt isolation or sandboxing where possible.
  

6. Build Trust Through Transparency

• Require AI providers to disclose how their models attribute sources, how they update their data, and their trustworthiness metrics.
  
• Use verifiable AI systems: for instance, research like VerifAI proposes verifying generative outputs via data management and consistency checks.
  
• Educate teams on AI limitations: make sure everyone understands hallucinations, bias, and misuse risk.
  

7. Regular Risk Reviews

• Conduct regular risk assessments for AI usage in your org. Include threat modeling (data poisoning, prompt injection, model abuse).
  
• Use established frameworks: base your process on trustworthiness standards like NIST AI Risk Management or ENISA, and incorporate human & social risk factors.
  
• Perform red-teaming or adversarial testing to simulate how an attacker might exploit your AI systems.
  

Emerging and Long-Term Risks to Watch

Beyond the immediate accuracy risk, businesses should also be aware of some less obvious but growing AI threats:

1. Shadow AI: Unmanaged AI use within organizations can lead to data leakage, regulatory non-compliance, or operational risk.
   
2. Supply Chain & Model Poisoning: Bad actors may infiltrate AI development pipelines or vector databases, introducing manipulated content.
   
3. Prompt Injection & Agent Misalignment: As AI agents become more autonomous, they may be manipulated through hidden instructions, misaligned goals, or compromised data.
   
4. Trust Erosion Through Failures: “Invisible failures” such as hallucinations or inaccurate replies erode trust, especially when end users don’t see obvious bugs.
   
5. Ethical & Governance Shortcomings: In the rush to adopt AI, businesses might neglect moral and ethical considerations — like overreliance, overtrust, or misuse — putting themselves at reputational and legal risk.
   

Case Study: When AI Search Goes Wrong

To illustrate the risk concretely:

• Scenario: A financial services firm uses ChatGPT in research mode to draft investment guidance.
  
• Issue: The AI suggests a strategy based on outdated regulatory limits.
  
• Impact: The firm issues client communications that do not comply with current ISA rules.
  
• Recovery: They must retract advice, revalidate with experts, and report the incident internally — causing reputational damage and lost trust.
  

This risk isn’t hypothetical. It’s grounded in the kind of error found in the investigation by Which? testers. 

Benefits of Controlled AI Search — When Done Right

Yes, AI web search has risks — but when managed carefully, it also offers substantial business value:

• Speed & Efficiency: AI can rapidly summarize complex legal or financial documents, giving teams a head start.
  
• Scalable Research: Teams can scale research efforts without hiring large specialist teams.
  
• Decision Support: When paired with human oversight, AI can provide valuable insights as a second opinion or draft baseline.
  
• Innovation Catalyst: Companies can use AI to explore scenarios, spot emerging trends, or test hypotheses more quickly.
  

Final Thoughts: Navigate with Caution — But Don’t Block AI

AI-powered web search represents a tremendous opportunity for businesses, but it’s not a plug-and-play solution. The recent investigation sounds a critical warning: high adoption does not equal high accuracy.

For the companies using AI, it is important for them to treat the AI search tools like any other fundamental system, with administration, supervision and examination. The risks, like hallucinations, malicious content, prompt injection and the opaque sourcing, are real, but they can be mitigated with thoughtful strategies. 

By building trust, staying vigilant, and enforcing rigour, the organisations can equip the whole power of AI web search without gambling with their credibility or adherence.

FAQs

Are all AI search tools equally risky?

No — accuracy levels vary significantly between tools. In a recent test, Perplexity outperformed ChatGPT and Meta AI on factual queries.

Can I rely on AI for legal or financial advice?

Not without human validation. Even the best models can misinterpret regulations or produce outdated info. Use AI as a starting point, but involve subject-matter experts.

How do prompt injection attacks work?

Prompt injection involves embedding hidden instructions in user input or documents that manipulate how an AI responds. Businesses need to secure prompt contexts and validate content rigorously.

How can we enforce governance around AI use?

Create a formal AI governance framework, define policies, restrict access to trusted models, and monitor usage. Shadow AI is a big risk — you need visibility.

What defenses exist against malicious or unsafe content?

Use retrieval-augmented LLMs, integrate URL-detection filters, maintain access controls over vector stores, and apply red-teaming or risk testing.

Will AI accuracy improve in the future?

Likely, but not automatically. Progress requires better model design, stronger data governance, increased transparency, and ongoing risk management. Research like VerifAI also suggests verifying generative outputs is a critical piece of the puzzle.