AI Is Running Wild — And It’s Your Business on the Line

Picture the following: You’re in charge of a famous hospital at the forefront of heart surgery, where surgeons perform life-saving operations every day. Would you hire a newly qualified surgeon with an outstanding academic record but no real-world experience—without rigorous security screening and thorough onboarding?

Because that’s basically what you’re doing when implementing AI without thorough safeguards. Are you ready to take the fall when a multimillion-dollar lawsuit lands on your desk because your AI slipped up?

The answer is obvious. Yet businesses all over the world are clamoring to plug in large language models (LLMs) and somehow expect everything to go well. After all, LLMs reduce costs, boost efficiency, and send your ROI through the roof, right?

As if that wasn’t enough, LLMs are remarkably easy to integrate and accessible to almost every company, large or small.

Gartner’s Hype Cycle effect shows that AI adoption is accelerating due to market competition and a mass drive toward the financial incentives it offers (Gartner, 2025). It is no surprise that companies specializing in AI are currently taking the lion’s share of funding, and understandably, you want to be part of that, too.

To use the analogy again: would you get this new young guy to perform daily heart operations at scale?

This article explores the real risks alongside some of the latest research and insights from our top AI experts, explaining how Exadel created Anchored AI to meet these challenges head-on. To continue our analogy, your hospital can now hire that brilliant but untested young surgeon—while keeping them on a tight leash at all times.

AI as a Teenager: The Reckless Years

We’re already hearing stories of those who have fallen into the trap of overzealously adopting AI. There was the major airline company that was fined millions for data breaches through the mishandling of vast amounts of customer information. The clever prankster who managed to convince a car company chatbot to agree to sell him a $76k car for a buck. The wrongful arrest of a heavily pregnant woman in Detroit for ‘carjacking’ when the AI-facial recognition system went off the rails one night. The well-known tech company whose LLM decided to leak its confidential code onto the internet. The amusing incident when a huge global delivery company’s chatbot started to call itself ‘useless,’ tell customers the company sucked, and then proceeded to insult them.

AI is not inherently secure and can be like an unruly teenager with a big ego who gets carried away, especially after a few drinks.

All joking aside, these kinds of security incidents are already costing companies millions of dollars…and their reputations. With increasingly stringent regulations, like the EU AI Act (2021), companies need to get on top of compliance.

LLMs process vast amounts of information and are designed to mimic human speech patterns. However, they lack understanding, control, and reasoning in real-world contexts. They are vulnerable to manipulation, can spout misinformation, and behave unpredictably.

Insights from risk data of surveyed companies suggest that the most cited AI risks are Inaccuracy (56%), cybersecurity (52%), Regulatory Compliance (51%), and IP Infringement (46%) (McKinsey, 2024).

Prime culprits of these risks include:

• Data Leaks — AI models don’t think before they respond and act. They can reveal sensitive data.
• AI Hallucinations — LLMs are advanced probability machines at heart—not engines of truth. There is always a chance they will produce incorrect or misleading information.
• Lack of Customization — AI models must work differently across industries and services. They can’t perform unless adapted to specific needs. They are not off-the-shelf solutions.
• Bias and Lack of Ethical Awareness — AI reflects the biases inherent in its training data. If trained on biased data, it will perpetuate them.
• Hacking and Prompt Injection Attacks — Cybercriminals can manipulate input prompts reasonably easily to get LLMs to reveal sensitive data or give them illicit access and privileges.
• False Sense of Security — Built-in security features from most AI providers offer only superficial protections that are easily bypassed.

At Exadel we’ve been delving into breaking down AI risks into granular form and working on mitigating them.

From Reckless to Responsible: Taming the Teen

The reactive approach to AI security is already a thing of the past—and too costly for starters. Ensuring AI is implemented safely and securely is like setting ground rules for teenagers before letting them roam freely.

Our team brings together Ph.D.-level AI engineers and security specialists who take a proactive approach to research. We’ve contributed to the field with significant publications, including:

• Explainable Machine Learning in Medicine (Przystalski & Thanki, 2024)
• Building Personality-Driven Language Models: How Neurotic is ChatGPT? (Przystalski et al, 2024).

These contributions come from our passion for advancing AI understanding. We help our clients enjoy AI’s full benefits while steering clear of its pitfalls.

Shortcuts or half-measures won’t win the game.

Qi et al. (2025) point out that a critical issue for LLMs is “shallow safety alignment,” basically applying safety mechanisms superficially and still leaving key vulnerabilities. Their study reminds us that AI implementation isn’t about paying lip service to security concerns. They assert that deepening safety alignment beyond the first few tokens through data augmentation, fine-tuning, and enhanced LLM robustness will be a good start to fend off adversarial attacks and avoid unintended malignant outputs.

Anchored AI – Setting Some Ground Rules

Think of Anchored AI as a sophisticated, responsible guardianship setting firm boundaries and enforcing discipline while knowing how to adjust the rules to different circumstances. To be clear, it’s not a plug-and-play product. It’s an approach that layers security into the AI we build to ensure that the horror stories from the intro don’t become nightmares on our customers’ Elm Streets.

Anchored AI’s Multi-Layered Protection:

• Input filtering — Screens user queries before they go out, to detect and control risky prompts.
• Processing Layer Security — Masks sensitive data, applies business-specific rules, and prevents unauthorized data access.
• Fact-Checking and Output Validation — Detects hallucinations by checking hallucinations against facts, not just probability equations. Verifies and ensures compliance with company policies.
• Enterprise-Wide AI Security Gateway — Controls how employees can interact with third-party AI tools, helping to prevent unauthorized leaks or access vulnerabilities.

Governance

Today, more than ever, companies have to manage governance to keep the wolves at bay. AI needs a structured framework within which to behave itself. Remember, if it goes rogue, it will cause you huge reputational, legal, and financial damage.

We must go beyond just risk mitigation—it’s about creating stable, controlled environments in which businesses can leverage the potential of AI, boost efficiency, and increase ROI, without falling foul.

Four Pillars of AI Governance:

• Transparency—Businesses must better comprehend how their AI makes decisions to prevent unpredictable actions and put guardrails where they belong.
• Accountability—Organizations need clear responsibility for AI-driven outcomes and ensure oversight, including direct human oversight of critical applications.
• Data Privacy and Security—AI models must align with global and region-specific regulations, like GDPR, and keep up with changing legislation and policy.
• Proactive Risk Assessments—Businesses cannot wait for something to happen. We can’t stress this enough. Continual monitoring, auditing, and fine-tuning of AI inputs, operations, and outputs are key. Teens don’t behave themselves because they’re told once. Ongoing guardrails are essential.

Exadel’s team monitors the constantly evolving governance climate, and Anchored AI ensures governance through pre-emptive risk assessments, compliance audits, continuous AI monitoring, and threat detection—all through a carefully phased process.

As mentioned, Anchored AI isn’t a plug-and-play solution like a periodically updated antivirus—it’s an approach and an ecosystem we tailor for our clients—one that integrates with your platform and operations and creates a future-proof alignment with industry best practices and the latest global governance demands.

Raising Your Teen Not to Be a Rebel

AI is here to stay. The question now is whether you raise it responsibly to be a productive family member—or a drain on your resources and reputation.

The risks are clear: millions in fines, reputational damage, and loss of customer trust.

The benefits of proper AI and LLM integration are unmatched:

• Smarter, safer, super-efficient operations and services.
• Confident AI decision-making based on accurate data evaluation.
• Regulatory Compliance without last-minute panic.
• AI that enhances every aspect of your business rather than putting you in daily danger.

That’s where Exadel’s experts step in with Anchored AI—ensuring your AI matures into a secure, reliable asset, not a liability that has you constantly on edge, fearing the next run-in with trouble.

How Businesses Can Get Started Right Now

Many companies seem hesitant about the process of integrating AI securely with an approach like Anchored AI. In essence, the roadmap is quite straightforward; however, the sophistication lies in the expertise required in consulting and implementation. This is an area where we excel. A simplified roadmap for AI integration with Anchored AI at Exadel would look something like this:

And for those who would like a little more detail about the whole process:

AI Security is a Leadership Issue

Embracing full-on AI security is not fundamentally an IT problem; it requires a transformational mindset and executive-level attention. It encompasses security, compliance, and business strategy in a way that, if not handled properly, could lead to significant damage control efforts in the future.

Anchored AI operates in a highly complex and expert field, and this article has only given you a taster of why it’s important and how you might approach thinking about it. For an in-depth overview of the Anchored AI approach and a conversation about how it can be customized to your specific platform, reach out to our team for a demo and a chat.

This is the time to get it right from the start.

References

Gartner. (n.d.). Gartner Hype Cycles™: All a CIO needs to know. Gartner. Retrieved March 2025 from https://www.gartner.com/en/insights/gartner-hype-cycle

McKinsey (2024). The State of AI in Early 2024. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

Przystalski, K., & Thanki, R. M. (2024). Explainable machine learning in medicine. Springer.

Przystalski, K., Argasiński, J. K., Lipp, N., & Pacholczyk, D. (2024). Building personality-driven language models: How neurotic is ChatGPT? Springer.

Qi, X., Panda, A., Lyu, K., Ma, X., Roy, S., Beirami, A., Mittal, P., & Henderson, P. (2025). Safety alignment should be made more than just a few tokens deep. ICLR 2025. Retrieved from https://openreview.net/forum?id=6Mxhg9PtDE&s=09