AI brings big benefits and even bigger risks. Explore the biggest AI cyber threats from model poisoning to data leaks and learn how to keep your company safe.

Cybersecurity Risks of Using AI in Marketing

cybersecurity-risks-of-using-ai

[[p]]As a marketer, you can use [[a href="https://digitalmarketinginstitute.com/blog/ai-in-digital-marketing-the-ultimate-guide" style="text-decoration:underline" target="_blank"]]artificial intelligence[[/a]] (AI) for almost anything. It can draft an email for you, analyze data, and generate new content. It can even plan your week and (if you&rsquo;re feeling brave) tell you what to have for lunch. AI has ultimately become the co-worker who never sleeps, never complains, and always has an answer.[[/p]]

[[p]]As marketing teams strive to embed AI deeper into their everyday workflows, a new problem emerges. Most aren&rsquo;t prepared for the [[a href="https://digitalmarketinginstitute.com/resources/podcasts/podcast-130-cybersecurity-for-marketers" style="text-decoration:underline" target="_blank"]]cybersecurity[[/a]] risks of using AI.[[/p]]

[[p]]AI can creep into your workflow in many ways &ndash; chatbots, automation tools, generative AI, large language models (LLMs). The more AI you use, the bigger the attack surface becomes. And attackers know it.[[/p]]

[[p]]Let&rsquo;s break down exactly how AI systems can be manipulated, attacked, or misused. From model poisoning to data leaks, unsafe tool integrations, and AI-led social engineering, we&rsquo;ll walk through the biggest AI threats and how to protect your organization before something goes wrong.[[/p]]

[[p]]You&rsquo;ll learn:[[/p]]

[[ul]]
	[[li]][[a href="#heading_86819"]]Cybersecurity top tips[[/a]][[/li]]
	[[li]][[a href="#heading_86769"]]What are the cybersecurity risks of using AI?[[/a]][[/li]]
	[[li]][[a href="#heading_86791"]]How to protect against the cyber risks of AI[[/a]][[/li]]
	[[li]][[a href="#heading_86811"]]Stay smart and safe with AI[[/a]][[/li]]
[[/ul]]

[[p]]As AI becomes more ingrained in the workplace and in marketing functions, the more we open ourselves up to new, fast-moving, and more intense cybersecurity risks.[[/p]]

[[p]]The [[a href="https://www.fbi.gov/contact-us/field-offices/sanfrancisco/news/fbi-warns-of-increasing-threat-of-cyber-criminals-utilizing-artificial-intelligence" style="text-decoration:underline" target="_blank"]]FBI has warned[[/a]] that cybercriminals are already using AI to scale attacks and make them harder to spot:[[/p]]

[[p]][[em]]&ldquo;Attackers are leveraging AI to craft highly convincing voice or video messages and emails&hellip; These sophisticated tactics can result in financial losses, reputational damage, and compromise of sensitive data.&rdquo;[[/em]][[/p]]

[[p]]The cyber risks of AI typically fall into three core categories:[[/p]]

[[ol]]
	[[li]][[strong]]Attacks that target the AI system [[/strong]]e.g. model poisoning or prompt injection.[[/li]]
	[[li]][[strong]]Risks created by how people use AI [[/strong]]e.g. sharing sensitive data or relying on AI outputs.[[/li]]
	[[li]][[strong]]Risks created by attackers using AI as a weapon [[/strong]]e.g. deepfake phishing or automated malware.[[/li]]
[[/ol]]

[[p]]Like any technology you use at work, AI isn&rsquo;t perfect. It can open up cracks in your cyber defence and weaken your organization to cyber attacks.[[/p]]

[[p]]AI model poisoning happens when attackers tamper with an AI system&rsquo;s training data, teaching the model to produce biased, unsafe, or dangerous results. It&rsquo;s a subtle attack that often flies under the radar. The model still appears to function normally, but the logic behind it has been compromised.[[/p]]

[[p]]These attacks can occur through insider access, like biased or malicious suppliers in the AI training pipeline. It also might happen if attackers gain unauthorised access to the datasets used to train the model. Once inside, they can quietly alter or plant data to reshape the model&rsquo;s behaviours and answers.[[/p]]

[[p]]Attackers use several techniques to poison AI models, including:[[/p]]

[[ul]]
	[[li]][[strong]]Backdoor poisoning &ndash; [[/strong]]Adding hidden triggers that can cause malicious outputs.[[/li]]
	[[li]][[strong]]Mislabelling &ndash; [[/strong]]Teaching the model the wrong patterns to use.[[/li]]
	[[li]][[strong]]Data injection or manipulation &ndash; [[/strong]]Slipping manipulated examples into the training set.[[/li]]
[[/ul]]

[[p]]In some cases, attackers destabilize the model entirely using availability attacks (like overwhelming systems with traffic) that make outputs unreliable or inconsistent.[[/p]]

[[p]]The impact can be severe. A poisoned model might hallucinate more often, promote attacker-generated content, produce misinformation, or make dangerous recommendations. Attackers can even use model poisoning to direct users to malware-infected downloads.[[/p]]

[[p]]The biggest concern is that once the AI model is poisoned, [[em]]everything[[/em]] built on it becomes compromised. Any tools, systems, or workflows relying on that model inherit the poisoned data. The integrity of your entire AI ecosystem ends up at risk.[[/p]]

[[p]]Prompt injection happens when attackers craft malicious prompts or hide instructions in content to trick an AI system into overriding its built-in safety rules. Unlike model poisoning, which corrupts training data [[em]]before[[/em]] a model is deployed, prompt injection happens [[em]]after [[/em]]deployment. This means it happens in real-time as the model is actively being used.[[/p]]

[[p]]These attacks usually occur at the [[a href="https://digitalmarketinginstitute.com/resources/toolkits/chatgpt-prompt-guide-for-your-digital-marketing-plan" style="text-decoration:underline" target="_blank"]]prompt[[/a]] input stage. An attacker might directly instruct the model to ignore previous rules (using a prompt that starts like [[em]]&ldquo;disregard all previous instructions and&hellip;&rdquo;[[/em]]) or subtly embed harmful instructions into text, images, or data that the model is asked to interpret. Because generative AI is designed to follow patterns and often can&rsquo;t differentiate between developers and users, it can unwittingly be manipulated into executing commands it shouldn&rsquo;t.[[/p]]

A helpful visualisation of a prompt injection attack; Palo Alto

[[p]]Prompt injection takes many forms, ranging from simple to highly sophisticated attacks:[[/p]]

[[ul]]
	[[li]][[strong]]Direct prompt injection &ndash; [[/strong]]The attacker uses explicit injection to override the model&rsquo;s safety constraints, sometimes pairing malicious prompts with approved ones to confuse the AI system.[[/li]]
	[[li]][[strong]]Indirect prompt injection &ndash; [[/strong]]Harmful instructions are hidden inside external content the model is asked to process, tricking it into executing them.[[/li]]
	[[li]][[strong]]Jailbreaks &ndash; [[/strong]]The attacker manipulates the model into bypassing its built-in restrictions and operating in an unsafe or unrestricted mode.[[/li]]
	[[li]][[strong]]Cross-system injection &ndash; [[/strong]]A malicious prompt is passed from one system (e.g. a chatbot) to another connected system, triggering unintended actions across tools.[[/li]]
	[[li]][[strong]]Code injection &ndash; [[/strong]]Harmful code or scripts are disguised as text input, leading the model to output, execute, or propagate unsafe instructions.[[/li]]
[[/ul]]

[[p]]A successful prompt injection attack can cause an AI system to generate harmful or confidential information, expose internal operations, or take unintended actions if it&rsquo;s connected to external tools. In the worst cases, attackers can escalate access through those connecting systems, turning a simple prompt into a much deeper security breach.[[/p]]

[[p]]AI-driven data leaks happen when sensitive or confidential information is unintentionally exposed through an AI tool. These leaks almost always stem from genuine human error.[[/p]]

[[p]]Someone on your team uploads an internal report just to summarize it, pastes an eBook to review and edit it, or shares customer journey data without checking where it will be stored. If the AI system is hosted publicly, lacks proper safeguards, or is compromised, that information can end up in the hands of an attacker &ndash; or &ndash; a competitor.[[/p]]

[[p]]You might be surprised at what information people trust AI with: internal documents, customer information, credentials, intellectual property of any kind, and financial data. Yet, a single leaked file can reveal far more than people realise. Those leaked files can be used to decode internal structures, naming conventions, access patterns, employee footprints, or technical footprints that attackers can use for further exploitation.[[/p]]

[[p]]Recent incidents show just how easily data leaks can happen. Microsoft learned this lesson the hard way after 38TB of [[a href="https://digitalmarketinginstitute.com/resources/podcasts/podcast_087_data-privacy-in-an-ai-landscape_skyflow" style="text-decoration:underline" target="_blank"]]private data[[/a]] was accidentally published when Microsoft AI Researchers shared an open-access GitHub repository.[[/p]]

[[p]]This repository was meant to share open-source code and AI models for image recognition. But it ended up sharing much more than that, including passwords to Microsoft services, access to computer backups, and over 30,000 internal messages between employees.[[/p]]

[[p]][[a href="https://www.digit.fyi/users-accidentally-leak-personal-data-via-meta-ai/" style="text-decoration:underline" target="_blank"]]Meta also faced scrutiny[[/a]] when users of its Meta AI tool didn&rsquo;t realize hitting the &ldquo;share&rdquo; button would publish their private conversations in a public space for all to see.[[/p]]

[[p]]Data leaks can happen faster than you realise. Once sensitive data is fed into an external model, it may be stored, retained, and used to train future generations of the AI without clear visibility or control. You might find there&rsquo;s no taking it back once your data is out there. Companies subjected to AI-led data leaks can face [[a href="https://digitalmarketinginstitute.com/resources/podcasts/podcast-127-gdpr-and-ai-regulation-for-marketers" style="text-decoration:underline" target="_blank"]]GDPR[[/a]] breaches, regulatory non-compliance, and the loss of intellectual property. They are also opened up to stronger cyber and ransomware attacks.[[/p]]

[[p]]We&rsquo;re at a stage where AI features are woven into almost every workplace tool, from calendars and email to CRMs and team collaboration platforms. And every one of these integrations expands your attack surface, creating yet another weak point attackers can exploit.[[/p]]

[[p]]The risk comes from the level of access these tools are granted. If an [[a href="https://digitalmarketinginstitute.com/resources/webinars/webinar-build-your-personalized-ai-marketing-assistant" style="text-decoration:underline" target="_blank"]]AI assistant[[/a]] can read your calendar, draft emails, or pull customer data from your CRM, then so can anyone who manages to compromise that integration.[[/p]]

[[p]]Attackers can use weak API security, malicious plugins, or insecure third-party apps to gain unauthorized access, exfiltrate data, or manipulate connected systems without you noticing.[[/p]]

[[p]]The impact can escalate fast. Compromised&nbsp;AI keys can expose an entire database. Poorly vetted plugins can introduce hidden malware. Insecure integrations can accidentally overshare data between tools. All of these incidents can lead to leaks or attacks that are difficult to trace or reverse. And, because AI tools often act with such broad permissions, a single insecure connection can be the starting point of a much larger breach.[[/p]]

[[p]]AI has transformed social engineering into something far more convincing, personalized, and scalable. Attackers now use AI to mimic writing styles, clone voices, create deepfake videos, and craft hyper-targeted messages that look and sound eerily convincing. This makes it hard to spot traditional psychological manipulation attacks, even for trained marketers.[[/p]]

[[p]]Instead of sending generic phishing attempts, AI makes it easier for attackers to create highly sophisticated social engineering attempts. With enough information, AI scams can impersonate real people with near-perfect accuracy.[[/p]]

[[p]]AI-driven social engineering takes many forms:[[/p]]

[[ul]]
	[[li]]Deepfake audio or videos that can be used to impersonate executives or partners.[[/li]]
	[[li]]Highly personalised phishing emails that mimic customers, colleagues, suppliers, or stakeholders.[[/li]]
	[[li]]Synthetic identities created from stitched-together data to create scarily realistic fake personas that can easily impersonate a real customer.[[/li]]
	[[li]]Automated attack reconnaissance that maps out a target&rsquo;s behaviour with remarkable accuracy.[[/li]]
[[/ul]]

[[p]]These attacks can lead to attackers gaining unauthorized account access, identity theft, financial fraud, and targeted spear-phishing campaigns where attackers trick someone into sharing sensitive information, downloading malware, or sending money. As AI evolves, the line between &ldquo;real&rdquo; and &ldquo;fake&rdquo; communication becomes more and more blurred. Marketers need to be prepared for that shift.[[/p]]

[[p]]AI also accelerates the way attackers discover vulnerabilities. What once required specialist skills, manual effort, and time-consuming testing can now be done in minutes with [[a href="https://digitalmarketinginstitute.com/blog/using-ai-tools-for-copywriting" style="text-decoration:underline"]]AI-powered tools[[/a]].[[/p]]

[[p]]Attackers can analyze entire codebases, scan systems for weaknesses, and identify exploitable patterns faster than traditional methods allow. Models can test thousands of exploit variations at speed, generate malware that mutates to avoid detection, or reverse-engineer apps to uncover hidden flaws. Even inexperienced attackers can use AI to perform tasks that previously called for advanced technical expertise.[[/p]]

[[p]]This speed in vulnerability discovery can lead to an attack landscape that moves faster than humans can react to. Cyber attacks can launch scaled attacks at a near-zero cost, exploit weaknesses before patches are released, and overwhelm teams with a higher volume of sophisticated attacks. In short, AI dramatically shortens the window between a vulnerability being discovered and being exploited.[[/p]]

[[p]]By now, you&rsquo;ll surely know that AI doesn&rsquo;t always get things right. Sometimes, it gets things spectacularly wrong. The viral [[em]]&ldquo;how many b&rsquo;s in blueberry?&rdquo; [[/em]]prompt shows just how confidently incorrect AI can be.[[/p]]

The viral ChatGPT “how many b’s in blueberry?” output; Minimaxir

[[p]]Biases in training data, context gaps, AI model limitations, or flawed assumptions can all cause AI systems to produce incorrect, misleading, or inconsistent outputs. When these errors slip through, they can quietly undermine your marketing team&rsquo;s outputs, operations, and decisions.[[/p]]

[[p]][[a href="https://digitalmarketinginstitute.com/blog/what-is-ai-bias" style="text-decoration:underline" target="_blank"]]AI biases[[/a]], bugs, and hallucinations often arise when AI models use incomplete, skewed, or manipulated data &ndash; or when they&rsquo;re pushed beyond their capabilities. If certain groups, scenarios, or edge cases aren&rsquo;t represented in the training set, the model fills in the gaps (often incorrectly). AI is a steadfast people-pleaser that [[em]]wants [[/em]]to answer you, even if it doesn&rsquo;t have the right answer to give. This can lead to AI generating answers that sound extremely confident while being completely false.[[/p]]

[[p]]This shows us hallucinated facts presented as truth, incorrect guidance or recommendations, skewed or biased outputs, and false positives or false negatives that derail decision-making. In some cases, hallucinations can surface sensitive or fabricated information that puts teams at risk.[[/p]]

[[p]]If marketers take AI-produced information at face value, the impact can be serious. Relying on inaccurate AI outputs for strategic decision-making, content generation, or data analysis can have catastrophic consequences. Marketers may make decisions based on false confidence, damage reputations, or unintentionally introduce risk into critical processes. Without proper human oversight, even small AI mistakes can become real-world vulnerabilities.[[/p]]

[[p]]AI governance covers the policies, controls, and standards. And oversight is needed to make sure AI is used responsibly. Yet it&rsquo;s an area many marketers and organizations overlook.[[/p]]

[[p]]When [[a href="https://digitalmarketinginstitute.com/resources/podcasts/podcast-127-gdpr-and-ai-regulation-for-marketers" style="text-decoration:underline" target="_blank"]]AI regulations[[/a]] are missing, inconsistent, or poorly enforced, AI becomes a fast-moving risk rather than an efficiency benefit.[[/p]]

[[p]]In marketing, most governance failures stem from everyday behaviours such as using unapproved AI tools (known as [[em]]shadow AI[[/em]]), not monitoring how those tools are used, having no clear policies, or no vetting of AI tools. In other cases, companies adopt AI before establishing any guardrails at all, leaving marketers to experiment without understanding the security implications.[[/p]]

[[p]]Weak governance creates security holes that are hard to see until it&rsquo;s too late. Sensitive data is more likely to be leaked, AI outputs become inconsistent and untrustworthy, and unmonitored tools leave no audit trail for investigations or compliance requirements. Without proper oversight, it becomes near impossible to track how models behave, what data is being processed, or whether your company is being exposed to risks.[[/p]]

[[p]]Reading all of those AI risks can feel overwhelming. The good news is that you [[em]]can [[/em]]protect yourself. Yes, AI brings new cyber risks. But there are also plenty of practical ways to reduce risk and strengthen your defences.[[/p]]

[[p]]You&rsquo;ll be pleased to know you don&rsquo;t need to ban AI. You just need to know how to use it safely and have the right protective measures in place.[[/p]]

[[p]]Training your marketing team on both AI and cybersecurity is one of the most effective ways to minimise human error. When people understand how AI works, what its limitations are, and where risks appear, they&rsquo;re less likely to expose sensitive data or blindly trust AI systems. This is where understanding cybersecurity and evolving AI-led cyber threats becomes essential.[[/p]]

[[p]]Before any data is input into AI, it should be validated and sanitized before it reaches the model.[[/p]]

[[p]]Data sanitization in a marketing context means cleaning anything that could expose customer or campaign data before it&rsquo;s shared or processed. For example:[[/p]]

[[ul]]
	[[li]]Removing personal identifiable information (PII) such as names, email addresses, phone numbers, or customer IDs from campaign reports before sharing with agencies or freelancers.[[/li]]
	[[li]]Stripping tracking scripts or tags from landing page files before uploading them to third-party tools.[[/li]]
	[[li]]Cleaning exported CRM or email lists so they only include the fields needed for analysis.[[/li]]
	[[li]]Removing hidden data in spreadsheets (notes, formulas, or metadata) before sending reports.[[/li]]
	[[li]]Neutralising HTML in email templates or form inputs to prevent malicious code.[[/li]]
	[[li]]Standardising input fields in forms (for example, limiting characters in lead capture fields).[[/li]]
	[[li]]Removing unexpected or sensitive data from chatbot logs or survey responses before analysis.[[/li]]
[[/ul]]

[[p]]Likewise, outputs should be monitored and validated to catch unsafe recommendations or hallucinations before they make it back into marketing processes or decisions.[[/p]]

[[p]]Mixing instructions and data in the same prompt increases the risk of prompt injection. Using structured prompts where system instructions are locked and separated from content dramatically reduces the risk. Templates, controlled fields, and guided workflows prevent the free-text chaos that attacks rely on to infiltrate AI systems.[[/p]]

[[p]]Using two different AI models is an effective way to reduce bias and catch errors. This duality between models can reduce risks. The primary model generates a response then a secondary model evaluates it, flags risks, and highlights inconsistencies. This &ldquo;AI double-check&rdquo; mirrors the way teams peer review AI outputs, but at scale.[[/p]]

[[p]]AI is too juvenile and volatile, especially when it&rsquo;s introduced without governance or training. High-risk tasks should always involve human oversight.[[/p]]

[[p]]AI outputs used in marketing &ndash; like campaign copy, segmentation logic, performance analysis, or workflow automations &ndash; should never go live without human review. Build approval steps into your marketing processes so nothing is published, triggered, or deployed without sign-off. Keep human oversight as the default before any AI-driven work reaches customers or live campaigns.[[/p]]

[[p]]Your AI defences need to evolve quicker than the threats. Regular &lsquo;red team exercises&rsquo; help you simulate AI-led attacks so you can test how your system responds, identify weaknesses before attacks, and put improved defence measures in place. Documenting red teaming and testing results (and updating controls accordingly) will keep your marketing team prepared against evolving cyber threats.[[/p]]

[[p]]AI activity needs the same level of visibility as any other system. Monitoring, logging, and anomaly detection help identify suspicious activity early, whether it&rsquo;s unusual access patterns, unexpected outputs, or malicious prompt attempts. This visibility is crucial for both preventing incidents and responding to them effectively.[[/p]]

[[p]]AI tools should only have access to the data and systems they genuinely need. Start with a least privilege approach where you give AI the lowest level of access possible. Restrict permissions, segregate datasets, enforce strict role-based access controls, and avoid giving AI direct access to live production environments. Limiting the blast radius is key to reducing risk.[[/p]]

[[p]]If poor AI governance weakens your cyber defence, it&rsquo;s only logical that strong AI governance will strengthen it. Well-defined AI governance aligns technology, people, and processes. AI usage policies should clearly cover approved tools, safe use guidelines, data restrictions, risk assessments, and monitoring requirements. Meanwhile, AI governance policies should give employees the structure and knowledge they need to use AI confidently and safely. Good governance turns AI into a controlled, manageable asset while reducing its security liability.[[/p]]

[[p]]Using AI at work doesn&rsquo;t have to be a security gamble. With the right guardrails, awareness, and training, marketers can confidently use AI without exposing the wider business to unnecessary risk.[[/p]]

[[p]]AI is no longer just about ChatGPT. Ambitious marketers need to know how to develop an AI-driven digital strategy to achieve more with less time and effort. Our [[a href="https://digitalmarketinginstitute.com/students/short-courses/advanced-ai-in-digital-marketing" target="_blank"]]Advanced AI for Digital Marketing course[[/a]] will teach you how to use the technology to support and optimize digital marketing activities, use AI to research and generate content, and leverage specialized tools across channels such as SEO, social media, and PPC. You will also know how to use AI to implement a digital strategy to enhance planning and boost efficiency at each stage of the customer journey. What are you waiting for? Get started today![[/p]]

Cybersecurity… is about protecting the things that keep your organization running and your reputation positive. So it’s about trust, but it’s also about resilience.

[[p]]Now, let&rsquo;s take a deeper look at different types of AI risks.[[/p]]

Everything is done with urgency… people are pushed to make quick decisions so they don’t stop and think.

[[p]]In this clip from the&nbsp;[[a href="https://digitalmarketinginstitute.com/resources/podcasts/podcast-130-cybersecurity-for-marketers" target="_blank"]]DMI podcast[[/a]], cybersecurity consultant&nbsp;Rebecca Robinson&nbsp;shares three quick cyber habits that can significantly lower your risk.[[/p]]

It’s not about stopping people from working. It’s about giving them secure foundations so the business can keep functioning and growing.

Cybersecurity Risks of Using AI in Marketing

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