Issue #7 You’ve Been HACKED
The hidden cyber risks of the AI that leave businesses exposed
A Cautionary Tale in the Age of AI
In early 2024, an employee at a UK firm hopped on what seemed like a routine video call with senior management. Minutes later, $25 million was wired to criminals. The twist?
Those “managers” on the call were AI-generated deepfakes.
It was a reminder that as businesses race to integrate artificial intelligence into their operations, they may also be unwittingly opening the door to new kinds of cyber risks.
From chatbots that spill corporate secrets to fraudsters cloning CEOs’ voices, the AI gold rush has a dark side.
This article explores the most common cybersecurity mistakes and vulnerabilities emerging from the corporate adoption of AI – all based on real events and expert research from 2024–2025 – and, importantly, how to avoid these pitfalls.
Leaky Chatbots and Data Nightmares
Employees (and employers) rush to save time and boost productivity and executives alike have been feeding confidential data into generative AI tools.
A practice that it is often with disastrous.
In fact, a recent analysis found 8.5% of employee prompts to popular AI models included sensitive data. Workers have unwittingly pasted in everything from customer billing details and insurance claims to internal financial reports and even network passwords.
Alarmingly, about 7% of these sensitive prompts contained security-related information (like penetration test results and network configurations) – essentially a blueprint an attacker could use to infiltrate the company.
Why is this happening? Many companies have not set clear guidelines or controls on AI usage, resulting in a Wild West of “shadow AI.” Some staff use free public chatbots without IT’s approval, and over half of recorded sensitive data leaks came via unsanctioned free AI services (for example, ChatGPT’s free tier).
Unlike vetted enterprise AI platforms, free consumer AI apps often retain user inputs for training, meaning that snippet of source code or strategy memo an employee entered might lurk in someone else’s AI results down the line.
In one high-profile 2023 case, Samsung engineers reportedly pasted confidential source code into ChatGPT – leading the company to ban the tool after discovering the leak. It’s no wonder that 27% of organizations have outright banned employee use of generative AI (at least temporarily) due to data privacy and security fears.
Even at companies that don’t go as far as a ban, leadership is growing uneasy. According to Cisco’s 2024 Data Privacy Benchmark study, nearly half of businesses admitted employees had entered non-public company information into AI tools.
Their top concerns?
The risk of exposing intellectual property (cited by 69% of companies) and sensitive information leaking to the public or competitors (68%).
Most firms are now scrambling to put guardrails in place:
63% have set rules on what data can or can’t be plugged into AI, and 61% restrict which AI tools employees can use.
Yet policies alone aren’t bulletproof if people are not aware or disciplined as nearly 45% of employees in these organizations have still tried entering staff or HR data into AI, and 48% have entered other confidential business info.
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When AI Gets “Brainwashed”
Handing an AI model the keys to your business processes can backfire if bad actors figure out how to manipulate that model.
A new class of exploits, ominously nicknamed “prompt injection,” has emerged as a major threat as companies deploy AI-powered chatbots, assistants, and decision-makers.
Prompt injection is essentially tricking the AI into doing something it shouldn’t by feeding it malicious or cleverly crafted input. It’s the AI equivalent of a social engineering attack – and 2024 saw a seven-fold spike in prompt-injection incidents as businesses rushed AI features to market.
How does this look in practice?
Imagine your company launches an AI chatbot to help customers, connected to internal systems for convenience. A hacker might send the bot a message that secretly instructs it to ignore its safety guardrails or expose private data.
In trials, researchers have shown it’s possible to embed hidden instructions in user inputs (even invisibly in text or images) that cause AI models to “misbehave” by revealing other users’ order details to executing unauthorized actions.
The problem got so common that the venerable OWASP foundation (known for web security) published a “Top 10” list of LLM (Large Language Model) risks, with prompt injection at the very top.
One contributing factor is the lack of monitoring and tooling around these new AI systems. Many organizations rolled out experimental AI tools without the usual security checkpoints.
Most lacked ways to detect rogue AI endpoints popping up or to sanitize prompts and outputs, according to Cloudflare’s analysis. That same report noted tens of thousands of daily attempts to “jailbreak” public AI APIs – essentially users (or bots) trying to punch through the AI’s restrictions. In short, attackers are actively tugging at the loose threads of corporate AI, looking for a snag.
The Trojan AI: Hidden Vulnerabilities
Integrating AI solutions isn’t just about building your own models. Many businesses are pulling in pre-trained models and AI services from open-source communities and third-party vendors.
This can be a double-edged sword. In the same way a malicious library can introduce a backdoor in software, a compromised AI model can smuggle in hidden vulnerabilities – truly a Trojan Horse scenario.
The tricky part is that an AI model’s “behavior” is code in itself, but far more opaque than a standard program. A model might perform brilliantly on regular tasks, but harbor a secret functionality that only activates on a specific trigger input (for example, a seemingly innocuous phrase or image).
When activated, this backdoor could do something nasty like leaking sensitive data, misclassifying critical inputs, or even running unauthorized operations. And unlike traditional malware, you can’t simply scan a neural network’s millions of weights with antivirus software – the malicious tweak is effectively camouflaged in a jungle of numbers.
It might sound like science fiction, but researchers proved otherwise in 2024. Security firm JFrog discovered that around 400 AI models on Hugging Face—a popular open-source hub—were laced with malicious code. In other words, hundreds of “free” models were booby-trapped, waiting for someone to download them. Trend Micro calls this the hidden supply chain risk of AI: with most tech firms now leaning on open-source models, a single poisoned file could slip unnoticed into products or analytics pipelines.
The danger doesn’t stop there. Many AI workflows depend on older file formats and libraries that can run code the moment they’re loaded. That means a model could look legitimate but, once opened, quietly execute malware and open a backdoor into company systems. It’s the software supply chain problem—only now with an AI twist.
AI-Enhanced Scams and Deepfake Deception
Not all AI threats come from rogue models. Some come from criminals using the technology to supercharge old tricks. Deepfakes now allow fraudsters to mimic a CEO’s face or voice with alarming accuracy.
In one case, a UK employee wired $25 million after joining what looked like a routine video call with senior management. The “managers” were AI-generated imposters. No malware, no hacking, just trust weaponized.
Regulators are taking notice. In 2024, the U.S. Treasury warned banks that deepfakes were being used to open fake accounts, forge IDs, and authorize fraudulent transfers. New York regulators urged firms to update their defenses, from customer verification to staff training, as these AI-assisted scams spread.
Meanwhile, phishing has evolved. Generative AI can write flawless scam emails, craft convincing websites, and even hold conversations with victims. Global phishing attacks jumped 60% year-on-year, fueled by AI-powered “vishing” calls and deepfake phishing pages. The result: scams that look and sound so real that even cautious employees might second-guess their instincts.
Automation Overconfidence
AI is fast, confident and often wrong. Think of it as a junior analyst who never admits doubt. Helpful most of the time, but when it slips, the mistakes can be costly.
One of the biggest risks is overreliance. Developers using AI coding assistants save hours, but almost half of AI-generated code has security flaws. In practice, that means bugs like weak authentication or unsafe database queries quietly slipping into production—ready to be exploited. What should be an efficiency win can quickly become a security nightmare.
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The problem isn’t limited to coding. Fraud detection systems miss edge cases, customer service bots mishandle sensitive data, and automated decision tools can make opaque, biased, or simply wrong calls. Because many AI systems operate as “black boxes,” errors go unnoticed until it’s too late. Regulators have warned that without proper oversight and audit trails, companies are still on the hook for any damage caused.
AI should assist, not replace, critical human judgment. Keep people in the loop for high-stakes tasks, demand transparency from AI tools, and treat automation as a co-pilot—not the captain.
Putting Guardrails on AI Adoption
In practical terms, here are some actionable guardrails every organization should consider when riding the AI wave:
Craft Clear AI Usage Policies: Establish what data employees can or cannot input into public AI tools, and offer secure alternatives for AI-assisted tasks. Make sure everyone from interns to executives knows the rules (e.g., “Don’t paste client data into ChatGPT”).
Train for AI-Awareness: Update your cybersecurity awareness training to include AI-specific scenarios – from spotting deepfake scams to recognizing when a chatbot might be manipulated. Regular drills or tips can keep staff vigilant against AI-enhanced fraud.
Secure Your AI Supply Chain: Treat external models and AI services like volatile imports. Vet vendors for security practices, prefer well-audited models, and use tools (or third-party audits) to scan for backdoors in AI models you incorporate. If you’re using open-source, pin versions and monitor community reports for any suspect behavior.
Test and Red-Team Your AI Systems: Before deploying an AI chatbot or tool, have your security team (or an external evaluator) attempt known attacks like prompt injection, data extraction, or feeding adversarial inputs. It’s better you find the flaw in your AI system than the attacker does.
Maintain Human Oversight: Implement a “human-in-the-loop” for critical AI decisions. For instance, if AI flags a transaction as safe that’s above a certain amount, have a person double-check. If AI writes code, have a developer review it – or run it through rigorous automated security testing.
Plan for the “What If”: Incorporate AI failure scenarios into your incident response plans. What if your AI malfunctions or is sabotaged – do you have a manual override? Much like a pilot has training to fly without autopilot, ensure your organization can switch to contingency procedures if an AI system goes awry.
By putting these safeguards in place, companies can enjoy the productivity and innovation gains of AI while drastically minimizing the risks. The key is to be proactive and realistic: assume AI will make mistakes or be targeted, and build your defenses accordingly.
As the saying goes, trust is good, but control is better.
With AI, you need a healthy mix of both. The businesses that thrive will be those that leverage AI’s strengths without leaving their blind side exposed…
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