Rushing Into AI: What We’re Missing When We Forget About Security

AI adoption is booming, but if you're not paying attention to security risks, you're gambling with your future. Here’s what decision-makers and cybersecurity leaders must understand now, before it's too late.

AuthorAuthor

Elsworth Anthony & Code & Culture
May 28, 2025

As someone who spends most of my time work in the technology industry and business, I’m fascinated by the rapid integration of artificial intelligence into business operations. AI is no longer hype, it’s here, and it's powerful. But like most things that promise major transformation, AI comes with risks that are all too easy to overlook in the race to “innovate or die.”

Recently, I came across an article on CSO Online titled “8 Security Risks Overlooked in the Rush to Implement AI” that really hit home. It’s the kind of piece that makes you stop and ask: “Are we building a smarter future, or just a more vulnerable one?”

Allow me to break down the core insights of that article and expand on them through the lens of a business development lead working with security-minded partners, advisors, and IT decision-makers every day.

1. Data Exposure from Oversharing

One of the biggest, yet least understood risks of AI integration is the inadvertent exposure of sensitive data. It’s not always a breach, it could be something as simple as a well-meaning employee pasting proprietary code into ChatGPT to debug it, or uploading customer data into an AI tool to “speed up analysis.”

The CSO article rightly points out that without strong governance, organizations can easily lose control over what information is being fed into third-party tools. Once it’s out there, it’s out there.

What You Should Do:

  • Restrict AI use cases to non-sensitive workloads unless you have explicit internal agreements and controls.

  • Deploy data loss prevention (DLP) policies in tools like Microsoft Purview to detect when sensitive information is shared.

  • Train your teams, awareness is your first line of defence.

2. Shadow AI and Unauthorized Adoption

In the same way “shadow IT” haunted CISOs over the past decade, we now have “shadow AI.” Employees are adopting free or low-cost AI tools on their own, often without understanding the implications.

The security team can’t protect what it can’t see. These tools might not meet your data protection standards or worse, could be collecting and storing data in ways that violate compliance requirements.

What You Should Do:

  • Scan your environment for AI tool usage with security solutions that monitor browser and application behaviour.

  • Build a sanctioned AI tools list and make it clear which platforms are approved for internal use.

  • Give people alternatives, sanctioned, safe tools for productivity, content generation, or analytics so they don’t go rogue.

3. Poisoned Training Data

Garbage in, garbage out. If an AI model is trained on flawed, malicious, or manipulated data, its output can be compromised. Worse, adversaries can actively “poison” training datasets especially in open-source models to embed vulnerabilities or skew results.

This isn’t theoretical. Microsoft and others have reported real-world scenarios where tainted datasets led to exploitable behaviours.

What You Should Do:

  • Vet your data sources for training or fine tuning AI.

  • Avoid scraping public web content blindly; use curated, verified datasets.

  • Implement anomaly detection in your models to catch unexpected or harmful behaviour early.

4. Model Theft and Reverse Engineering

Your AI models, particularly if you’ve invested in training them on proprietary data, are intellectual property. But if you expose APIs or endpoints without protection, bad actors can reverse engineer the models or even clone them.

According to the article, adversaries can make thousands of queries to a model and use the responses to reconstruct its logic. Think of it like industrial espionage, only faster and easier.

What You Should Do:

  • Throttle API access and implement usage monitoring to detect high-frequency querying.

  • Use watermarking and fingerprinting techniques to prove ownership of your models.

  • Deploy models behind secure interfaces and authenticate all interactions.

5. Vulnerabilities in Third-Party AI Tools

Every third party AI service you use is a potential attack surface. If that vendor experiences a breach, your data and the trust you’ve built with your clients can go down with them.

And yet, in the gold rush to integrate generative AI into products and services, due diligence often gets tossed aside.

What You Should Do:

  • Treat AI vendors like any other critical supply chain partner. Demand to see their security practices, audit reports, and breach notification policies.

  • Build a formal risk review process for any new AI tools or APIs being introduced to your stack.

  • Consider alternatives like hosting your own LLM (large language model) on-prem or in a private Azure instance using services like Azure OpenAI.

6. Model Hallucination and Misinformation

This is perhaps the most misunderstood AI flaw: hallucinations. AI doesn’t always tell the truth it just returns what sounds like a confident answer. That’s dangerous in business settings.

Imagine an AI providing incorrect compliance guidance or mislabeling a cyberthreat because it “thinks” it’s right. If humans trust it blindly, decisions based on hallucinations could lead to legal, operational, or financial damage.

What You Should Do:

  • Keep a human in the loop. Always review AI generated results, especially in high-stakes use cases.

  • Use retrieval-augmented generation (RAG) frameworks that pull real-time, verified data to ground LLM responses.

  • Implement disclaimers and review checkpoints in AI-powered workflows.

7. Over-reliance on AI for Threat Detection

It’s tempting to think that AI can do it all especially in cybersecurity. But AI isn’t a silver bullet. If you lean too hard on machine learning for threat detection without proper tuning, oversight, and context, you might miss the signals that matter most.

There’s also the risk of attackers learning how your detection model works and developing “adversarial attacks” that bypass it.

What You Should Do:

  • Use AI as an augmentation, not a replacement. Keep human analysts in the loop.

  • Conduct regular red teaming exercises to test AI-powered defences.

  • Invest in explainable AI (XAI) to understand how your threat models make decisions and where they might fail.

8. Lack of a Governance Framework

Finally, and perhaps most importantly, too many organizations are deploying AI without a proper governance framework. Who owns it? Who’s accountable for outcomes? What happens when it goes wrong?

The article emphasizes that AI governance must span security, legal, ethics, and operations. Otherwise, you risk running a black-box engine with no brakes.

What You Should Do:

  • Establish a cross-functional AI governance committee.

  • Document policies around acceptable use, risk tolerance, model retraining schedules, and incident response.

  • Map AI use cases to business risk to prioritize controls and audits accordingly.

You Can’t Secure What You Don’t Understand

AI is here to stay. And its potential is massive from optimizing operations to unlocking new revenue streams. But rushing into implementation without a security mindset is a recipe for disaster.

Whether you’re in a leadership role, managing IT infrastructure, or simply trying to understand how to adopt AI safely, you need to treat AI like any other critical system: with discipline, control, and continuous oversight.

If you're not having conversations about AI security governance, supply chain risks, and human oversight, you're not ready for real AI adoption. Full stop.

Practical Next Steps for Business Leaders

  1. Assess your current AI footprint. What tools are in use, officially or unofficially?

  2. Educate your teams. Awareness campaigns go a long way.

  3. Map out data flows. Know what’s being shared with external platforms.

  4. Launch a pilot AI security review. Start with your most visible use case.

  5. Plan for scale with security baked in. Governance should be part of your AI roadmap—not an afterthought.

In Closing

As we continue to explore AI’s promise, let’s not forget its perils. Let’s build smarter and safer.

If you're interested in digging deeper into how to secure AI within your organization, I’d love to connect. Whether you're early in your journey or already experimenting with AI platforms, there are practical steps you can take today that will protect your organization tomorrow.

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