Security Risks in AI Applications (And How to Fix Them)

AI is moving fast. Security is struggling to keep up.

The actual situation which most organisations face at present time is represented by this statement. The organisation operates at an accelerated speed which enables them to implement AI systems while developing AI capabilities and incorporating machine learning into all their products and business operations. Security teams face difficulties because they must deal with multiple tasks which result in security being treated as a secondary priority.

According to a 2024 report by the National Cybersecurity Alliance, 75% of security professionals say AI-powered attacks are becoming more sophisticated faster than their defences can adapt. The actual security danger arises from the existing security management practices which fail to keep pace with the rapid adoption of artificial intelligence technologies.

So what are the actual security risks with AI, and what does fixing them look like in practice?

Why AI Applications Create a Different Kind of Security Problem

Traditional application security is a well-understood discipline. You secure the perimeter, validate inputs, manage access, encrypt sensitive data. The principles are established and the tools are mature.

AI applications introduce a different category of problem. The attack surface is broader, the failure modes are less predictable, and some of the most serious vulnerabilities aren't in the code at all; they're in the data, the model, and the way the system makes decisions.

Understanding what the security risks of artificial intelligence actually are requires thinking about security at every layer, not just the application layer.

The Most Serious AI Security Risks Right Now

1. Data Poisoning

AI models learn from data. Which means if an attacker can influence the training data, they can influence the model's behaviour, often in ways that are extremely difficult to detect.

Data poisoning attacks involve injecting malicious or manipulated data into a training dataset so the model learns incorrect patterns. The consequences range from subtly biased outputs to deliberately triggered misbehaviour when specific inputs are presented.

This AI security issue remains underrecognised because its risks emerge before software deployment. The system reaches its final state when it starts to operate in production, but tracking its initial damage causes remains difficult because of the tainted training dataset.

How to fix it: Implement strict data provenance and validation processes. You must establish complete knowledge of your training data origins which people can access and methods used to monitor data alterations. You must protect your training pipeline with the same security measures which you would use to secure your production environment.

2. Adversarial Attacks

Adversarial attacks exploit the way AI models process inputs. By making tiny, carefully calculated modifications to an input, changes that are often completely imperceptible to a human, an attacker can cause an AI system to produce wildly incorrect outputs.

The classic example is image recognition: a slight pixel-level modification to a photo of a stop sign causes an AI system to classify it as something else entirely. But adversarial attacks apply equally to text-based models, fraud detection systems, and any AI application that processes external inputs.

For businesses relying on AI for decisions, approvals, flagging, and classification, this is a serious AI security risk in applications that needs to be designed against, not just hoped away.

How to fix it: Adversarial robustness testing should be part of your model evaluation process. Test your models against known adversarial techniques before deployment, and monitor outputs in production for anomalies that might indicate an attack in progress.

3. Model Inversion and Data Extraction

This one surprises people. It's possible, under the right conditions, to reverse-engineer sensitive information from an AI model simply by querying it carefully.

Attackers perform model inversion attacks by submitting specific queries to a target model which provides output that allows them to recreate part of the model's training dataset. An attacker can obtain personal information about customers and medical records and financial information from training data which contains personally identifiable information.

The security risks of AI in healthcare make this particularly acute. Models trained on patient data, diagnostic records, or treatment histories carry significant privacy obligations, and model inversion is a real vector for violating them. The role of AI in cybersecurity is increasingly about defending against exactly these kinds of model-level attacks, not just traditional application vulnerabilities.

How to fix it: Differential privacy techniques can limit how much individual data points influence a model's outputs, making extraction significantly harder. Access controls on model queries, rate limiting, and output monitoring all add meaningful layers of defence.

4. Prompt Injection in LLM-Based Applications

If you're building applications on top of large language models, chatbots, AI assistants, and automated content systems, prompt injection is one of the most immediate AI security concerns you need to understand.

Prompt injection occurs when users create inputs which disrupt the normal operation of a model by delivering commands which the system should not handle. A customer-facing AI application enables users to bypass content filters while they extract hidden system prompts and force the AI system to generate results which operators specifically prohibited.

OWASP listed prompt injection as the number one security risk for LLM applications in 2024, which gives you a sense of how seriously the security community is taking it.

How to fix it: Input validation and sanitisation, strict separation between system instructions and user inputs, and output filtering are all part of the defence. Understanding the difference between AI application development tools and custom development matters here too; custom-built applications allow for security controls that off-the-shelf tools often can't accommodate.

5. Insecure API Integrations

Most AI applications require multiple components to function because they need external APIs and data sources plus third-party models and internal systems to operate. The connections to these systems create multiple security boundaries because insecure API integrations have become one of the most common security vulnerabilities that modern applications face.

The combination of poor authentication practices and excessive access permissions together with the absence of encrypted data transmission and insufficient API call logging creates multiple security vulnerabilities that attackers actively seek to exploit.

How to fix it: Apply the principle of least privilege to every API integration. AI components should only have access to exactly what they need, nothing more. Enforce HTTPS everywhere. Log and monitor API activity. And audit your integrations regularly, because the third-party services you connect to change their security posture over time.

6. Supply Chain Vulnerabilities

Developers of AI systems use open-source libraries together with pre-trained models and third-party tools as essential components of their work. The ecosystem possesses both strong capabilities and functions as a supply chain which brings its associated security risks.

The system becomes vulnerable to attacks through three main elements: a compromised open-source library and a backdoored pre-trained model and a malicious package that pretends to be a genuine dependency. Developers of AI systems experience development challenges because they need to create functioning products before they finish complete testing of all project components.

How to fix it: Treat your AI supply chain with the same scrutiny as any other software dependency. Use software composition analysis tools, verify the provenance of pre-trained models, and keep dependencies updated. This is a core part of responsible web application security services in AI contexts.

Security in Specific Contexts: Healthcare and Mobile

The security risks of AI in healthcare systems require special attention because the consequences of these risks create extremely serious dangers. Patient data is among the most sensitive personal information that exists. Regulatory frameworks like HIPAA in the US and equivalent legislation elsewhere impose strict obligations. The organisations responsible for the breach together with patients who are affected by the breach will face severe consequences. 

AI applications in healthcare face all of the described risks together with the added difficulty of combining with clinical systems and processing both sensitive data and operationally essential information and performing in areas where mistakes directly endanger patient safety.

Mobile application security presents its own layer of challenge for AI applications. On-device AI models can be extracted and reverse-engineered. Mobile apps are more exposed to adversarial inputs from untrusted environments. And the combination of personal data, location information, and AI-driven personalisation creates concentrated privacy risk that needs careful management.

Building Security In, Not Bolting It On

The consistent thread across all of these risks is that they're much easier to address at the design stage than after deployment. Security that gets retrofitted is always more expensive, less effective, and more disruptive than security that's built in from the start.

That means threat modelling before development begins. Security requirements defined alongside functional requirements. Penetration testing that specifically targets AI components, not just the application shell around them. And AI training and ongoing support that includes security awareness, because the humans operating these systems are always part of the security picture.

Building a genuinely secure AI application also requires honest capability assessment. The combination of AI expertise and security expertise needed to do this properly isn't always available in-house, which is where working with a team experienced in delivering advanced AI solutions with security embedded throughout the process makes a real difference.

The Bottom Line

AI applications aren't just software with some extra features. They introduce a genuinely different set of security challenges at the data layer, the model layer, the integration layer, and the application layer simultaneously.

Gartner predicts that by 2027, more than 40% of AI-related data breaches will be caused by improper use of generative AI across borders. The window for getting ahead of these risks is open, but it won't stay open indefinitely.

The businesses taking AI security seriously now, building proper controls, conducting real security testing, and treating model security with the same rigour as application security, are the ones that will scale their AI capabilities without the kind of incident that sets everything back.

At Dotsquares, security isn't a checkbox at the end of an AI project. It's part of how we build from day one. If you're developing AI applications and want to make sure the security foundations are solid, that's a conversation worth having sooner rather than later.