In just a few years, artificial intelligence has gone from an academic curiosity to the engine behind chatbots, recommendation systems, autonomous tools and even critical infrastructure. 

As organisations rushed to weave AI into their products, they opened up new security gaps that traditional defences weren’t built for. Over the past year I’ve seen developers blindsided by prompt injections, executives fooled by deepfakes and models sabotaged during training. 

To help you avoid these pitfalls, I’ve gathered ten of the most serious AI security risks and paired them with practical safeguards. These insights draw on real incidents, community research like the OWASP LLM Top 10 and my own experience building and testing machine‑learning systems.

1. Data poisoning weakens a model at the training stage

When teams train models on public or crowdsourced data, they assume most of that data is accurate. Attackers rely on that assumption. They slip harmful samples into the dataset to influence how the model behaves or to hide a backdoor that activates later.

The damage rarely shows up right away. A recommendation system may start promoting misleading content. AI models learn from massive datasets, a small number of poisoned records can pass unnoticed until the model makes a costly mistake.

How teams reduce the risk

Teams should rely on curated datasets and document the source of every data sample. Automated checks can flag records that behave differently from the rest of the dataset. Dataset versioning also helps teams return to a clean state when issues appear.

Methods like differential privacy and federated learning reduce the impact of any single record, which limits how much damage an attacker can cause. Many teams also train models with known adversarial inputs so the model learns to resist manipulation instead of absorbing it.

2. Model inversion and data leakage compromise privacy

Some attackers don’t care about your model; they want the data you used to train it. By repeatedly querying a model, they can reconstruct faces, email addresses or other sensitive records. 

Even without outright attacks, a chatty model might reveal proprietary information when asked the right question. In fields like healthcare or finance, such leaks can breach laws and shatter user trust.

Mitigation tips: Differential privacy adds controlled noise during training so individual training examples are hidden. Keep your model’s answers succinct – the less detail it gives, the harder it is to reverse‑engineer the data. 

Enforce authentication and throttle API requests to block automated inversion attempts. And always scrub sensitive information from both inputs and outputs using DLP tools.

3. Prompt injection subverts model behaviour

Large language models are wonderfully flexible – they follow natural‑language instructions with ease. That flexibility comes at a cost: a crafty user can embed hidden commands in their prompt or in an external document and trick the model into executing unintended actions. In 2024, researchers showed how Slack’s AI assistant could be coaxed into leaking private channel data.

Mitigation tips: Don’t feed the model raw user input. Strip out HTML tags, code fragments and other suspicious patterns, separate system prompts from user prompts and enforce strict input templates. 

Adopt a zero‑trust stance – every incoming prompt is untrusted until proven safe. Build guardrails that limit what the model can do based on who is asking, and run regular red‑team exercises to discover new injection techniques.

4. Model theft and IP leakage

A proprietary model can represent years of research and engineering. Yet anyone can try to reconstruct it by hammering your API with queries and building a surrogate.

Attackers have used this technique to clone commercial models and then use them to craft better attacks. High‑fidelity responses expose decision boundaries and make extraction easier.

Mitigation tips: Cap how many questions a user or IP address can ask and throttle abnormal request patterns. Embed watermarks or hidden signatures in responses so you can identify stolen outputs. 

Avoid returning verbose reasoning chains unless absolutely necessary. Finally, log and analyse queries to spot suspicious probing.

5. Adversarial examples and evasion attacks undermine trust

Sometimes the smallest tweak to an input – a sticker on a stop sign or a few pixels changed in an image – can make a model produce wildly wrong results. These adversarial examples reveal how brittle some models are and can help attackers bypass spam filters or content moderators.

Mitigation tips: Expose your model to adversarial examples during training and stress‑test it regularly. 

Choose architectures known to be more resilient to perturbations and normalise inputs or squeeze features to dampen malicious noise. 

Monitor live traffic for anomalies and build fail‑safes such as human review when confidence drops.

6. Supply-chain weaknesses can compromise your entire system

Most teams don’t build AI systems from the ground up. They rely on pre-trained models, open-source libraries, and public datasets to move faster. That speed comes with risk. If even one of those pieces contains malicious code or hidden behavior, it can affect everything built on top of it.

These issues don’t always announce themselves. A tainted model can work as expected for weeks or months before a hidden trigger activates. By the time teams notice, tracing the problem back to its source becomes difficult.

How teams reduce the risk

Teams should pull models, libraries, and datasets only from sources they trust and verify their integrity before use. A detailed inventory of every dependency helps teams understand what runs in production and where it came from.

Automated scans can catch known issues early, but regular updates matter just as much. When teams bring in high-risk third-party components, they should test them in isolation first. Red-team testing often helps uncover backdoors that standard checks miss.

7. Insecure APIs and integration points

Your model’s API is the front door to its logic. If that door is unsecured, attackers can steal your model, scrape data or inject malicious input. Generative APIs sometimes return so much context that they unwittingly reveal internal rules or private data.

Mitigation tips: Treat your AI API like any critical service: enforce authentication, use OAuth 2.0 or mutual TLS and implement IP whitelisting. Apply rate limits and logging, and watch for unusual traffic patterns. 

Enforce least‑privilege permissions so endpoints expose only necessary functionality. And never pipe model output directly into downstream systems without sanitising it first.

Even with strong API controls, attackers often gain access through compromised laptops or unmanaged devices. This is why many organisations pair API security with endpoint security controls that monitor device behaviour, block malware, and enforce access policies before requests ever reach the model.

8. Deepfakes and impersonation attacks break trust fast

The same tools people use for fun now help attackers copy voices, faces, and writing styles with unsettling accuracy. Criminals have cloned executives’ voices to approve fake wire transfers. Others have shared fabricated videos to damage reputations or spread false claims. As synthetic content fills inboxes and social feeds, spotting what’s real takes more effort than it used to.

How teams reduce the risk

Teams should rely on proof, not appearances. Digital watermarking and content provenance metadata help confirm where media came from and whether someone altered it. Detection tools can flag manipulated audio or video, but teams need to keep those tools updated as techniques change.

Training matters just as much. Employees should question unexpected requests, even when they sound familiar. For high-risk actions, teams should require multi-factor checks and out-of-band verification instead of trusting a single message, call, or clip.

9. Shadow AI and unauthorized tools

It’s tempting for employees to use off‑the‑shelf AI tools to boost productivity, but unsanctioned usage can leak proprietary data or violate compliance rules.

I’ve seen well‑meaning staff paste customer information into online chatbots without realising that their data may be stored and used for training. The rise of shadow AI mirrors the earlier shadow IT problem but with greater stakes.

Mitigation tips: Publish clear policies outlining which AI tools are approved and under what conditions. Maintain an inventory of AI assets and monitor networks for unapproved traffic. 

Provide training so employees understand the risks of sending sensitive data to external services. When unauthorized tools are discovered, act quickly to shut them down and assess what data may have been exposed.

10. Weak governance leaves AI systems unchecked

Many AI projects begin as small experiments. Over time, they move into production. Often, no one pauses to decide who owns the system or how the team should monitor it. 

When that happens, gaps appear fast. Teams may cross ethical lines or miss compliance rules without realizing it. A 2025 Darktrace survey showed that fewer than half of security professionals fully understand the AI systems they manage.

How teams reduce the risk

Ownership has to be clear early. If nobody owns the model, problems slip through fast. One person or team should stay accountable for where the data comes from, how the model is built, when it ships, and what happens after that.

Documentation shouldn’t read like a formality. It should answer simple questions: what does this model do, what data does it rely on, and where does it break down. If those answers aren’t easy to find, something is already wrong.

Bias checks and reviews can’t be a box you tick once and forget. Teams need to revisit them as the model changes and as new data comes in. Training helps here. When people actually understand how the system behaves, they notice issues sooner and don’t panic when something looks off.

None of this works without a solid technical base. Secure infrastructure makes governance possible. Access controls, logs, and audits aren’t optional extras. They’re what let teams trace mistakes, prove compliance, and fix issues before they turn into incidents.

Effective governance depends on secure infrastructure. Following established cloud security best practices helps teams enforce access controls, maintain audit trails, and meet compliance requirements.

Conclusion

Securing AI isn’t a one‑time task you tick off and move on from. It’s an ongoing discipline that spans data science, software engineering and cybersecurity. The risks above often interact: a prompt injection can lead to data leakage; an insecure API makes model theft trivial; deepfakes flourish when governance is weak.

That’s why defences need to be layered. Combine robust data pipelines, differential privacy, input validation, continuous monitoring, supply‑chain integrity, user education and strong governance to reduce your exposure. 

Keep testing – red‑team your models, scan your dependencies and stay plugged into the security community for emerging threats. Your users and your business depend on it.