What cybersecurity threats do organizations face from AI?

Phishing, ransomware, data theft, and AI-powered exploits are only a few threats organizations face. Learn how attackers use AI and how to fight back.

What cybersecurity threats do organizations face from AI?

Artificial intelligence is shaking up cybersecurity. It can strengthen defenses, yet becomes a dangerous weapon when misused. We can now detect threats faster and automate protection at scale. Yet the same technology is being exploited by attackers to craft convincing scams, launch automated malware, and manipulate systems in ways that are hard to spot. The result is a fast-changing threat landscape where AI acts as both the problem and the solution. Keep reading to see how attackers are already using it to sharpen their strategies.

AI-powered phishing and social engineering

Phishing has long been a top cybersecurity threat, but AI is taking it to another level. Instead of clumsy emails full of typos, attackers can now generate flawless messages tailored to specific targets. Large language models (LLMs) make it easy to mimic tone, style, and even regional language quirks.

Social engineering is also evolving. AI tools can clone voices to trick employees over the phone, or create deepfake videos that make scams far more believable. Combined with data scraped from social media, these tactics blur the line between authentic and fraudulent communication.

In early 2024, UK-based engineering firm Arup became the target of a highly sophisticated deepfake scam—an extreme case of AI-powered social engineering. An employee received what looked like a legitimate video call from senior management and the CFO. Trusting the apparent authority on the screen, they transferred a staggering $25 million. Only later did the company discover that the executives had been digitally fabricated using AI-generated deepfake technology.

Cases like this highlight how AI has supercharged phishing and social engineering, turning once-easy-to-spot scams into convincing, high-stakes cybersecurity threats.

Automated malware and ransomware development

AI is making it easier than ever for cybercriminals to launch attacks. In the past, creating sophisticated malware or ransomware required technical expertise and time. Now, AI tools can generate malicious code snippets, automate testing, and even adapt attacks to bypass defenses, all at speed and scale.

What makes this especially dangerous is the adaptability. Malware can be designed to “learn” from failed attempts, adjusting itself until it slips past detection systems. Ransomware campaigns can also become more targeted, with AI analyzing potential victims to decide on the highest-value targets and optimal ransom amounts.

A recent threat intelligence report from Anthropic reveals a disturbing development: AI models such as Claude and Claude Code have been leveraged by cybercriminal groups to automate the entire ransomware lifecycle, from crafting sophisticated malware to calculating ransom demands and even generating persuasive extortion messages. One such group deployed these tools to carry out coordinated ransomware campaigns affecting healthcare, government, and emergency services organizations.  

For organizations, this means the cybersecurity threat is no longer just from expert hackers, it’s from anyone with access to AI tools.

Adversarial attacks on MLMs

Machine learning models (MLMs) are powerful, but also vulnerable. Adversarial attacks exploit weaknesses in these models by feeding them carefully manipulated data. To a human, the input might look normal. To the model, it triggers errors or incorrect predictions.

For example, a subtle alteration to an image can trick a security camera into misidentifying someone. A poisoned dataset can quietly corrupt an AI fraud detection system, teaching it to overlook suspicious transactions. These attacks don’t just disrupt performance; they undermine trust in AI-powered security tools.

One such case was when Microsoft’s Azure AI Content Safety service was compromised by adversarial techniques that allowed attackers to manipulate content moderation models, bypassing safeguards meant to prevent harmful AI-generated content.  

Exploiting AI in cloud and IoT environments

Cloud platforms and IoT devices are now woven into almost every organization’s operations. AI plays a big role in managing these environments, optimizing workloads, detecting anomalies, and automating routine tasks. But the same AI can be turned against them.

Attackers are learning to exploit AI-powered orchestration tools in the cloud, tricking systems into misallocating resources or opening hidden entry points. In IoT, the stakes are even higher. Smart devices often have weaker security, and AI can be used to coordinate large-scale attacks by hijacking thousands of them at once. A single compromised sensor might seem trivial, but when networked together, these devices can give attackers enormous leverage.

In August 2025, researchers from Tel Aviv University, Technion, and SafeBreach demonstrated a sophisticated AI-driven attack on smart home devices powered by Google's Gemini AI. By embedding malicious instructions within Google Calendar events, they exploited indirect prompt-injection vulnerabilities to manipulate smart home systems. These attacks enabled unauthorized actions such as opening smart shutters, turning on a boiler, and sending offensive messages, all triggered by seemingly benign user inputs like “thanks” or “sure.”

Data privacy and intellectual property theft

AI depends on data, and that dependency creates dangerous exposure. Businesses now sit on mountains of sensitive data, everything from customer records to intellectual property. Cybercriminals can use AI to sift through stolen data faster, spotting valuable details that a human attacker might miss.

Intellectual property is another high-value target. AI tools can scrape confidential documents, design files, or source code, then repackage or leak them. In some cases, attackers may even use generative AI to reconstruct stolen data, making it harder to contain a breach.

What is interesting about this case is that even giants like Anthropic and Apple at some point became embroiled in controversies related to intellectual property, though not always from external cybercriminals.

• In 2025, Anthropic faced a class-action lawsuit for allegedly using unauthorized copyrighted books to train its AI, a case that called into question the ethics of data sourcing in AI development and the misuse of intellectual property for profit.

• Similarly, Apple was sued by authors who claimed that their copyrighted works were used without consent to train AI models, leading to legal action for unpaid use of intellectual property.

These high-profile cases show that even major companies, when leveraging vast datasets for AI innovation, can inadvertently (or in some cases, unintentionally) use intellectual property in ways that raise legal and ethical concerns.

Insider cybersecurity threats enhanced by AI tools

Not every cybersecurity threat comes from the outside. Employees, contractors, or partners can misuse AI (intentionally or unintentionally) in ways that put organizations at risk. An insider with malicious intent might lean on AI tools to bypass security controls, generate custom malware, or extract sensitive data more efficiently than ever before.

Even well-meaning staff can cause damage. For instance, uploading confidential reports into a public AI tool for “quick analysis” can inadvertently leak proprietary information. In 2023, Samsung engineers did exactly that, accidentally pasting sensitive source code and internal data into ChatGPT, creating the risk of intellectual property exposure outside the company. In regulated industries, mishandling data can trigger compliance breaches and result in severe penalties.

Building AI-resilient cybersecurity strategies

If attackers are using AI, defenders must do the same. Building resilience means going beyond traditional defenses and adopting strategies that anticipate adaptive, fast-moving cybersecurity threats.

A strong strategy is adopting Zero Trust security, where every user and device must earn trust, regardless of their position in the network. Combined with AI-powered monitoring, this model helps detect unusual behavior early. Organizations are also investing in AI-driven defense tools that learn from attacks in real time, allowing systems to counter new techniques as they emerge.

But technology isn’t enough. Employee training and governance play a crucial role. Teams need to understand the risks of feeding data into AI tools, while leaders must set clear policies for ethical and secure AI use. Regular audits, red-team exercises, and layered defense strategies ensure organizations don’t rely on a single line of protection. The future of cybersecurity isn’t about replacing humans with AI, but combining their strengths.