What is AI Security, and Why is it Important?

AI security is the process of using artificial intelligence in cybersecurity to protect systems from malicious attacks.

AI security covers two main areas:

1. Using AI in cybersecurity: By automating threat detection, prevention, and response, AI-powered systems help organisations respond to cyber threats quickly and accurately.

For instance, machine learning algorithms can analyse large volumes of data from your network — like traffic patterns, login attempts, or user behaviours — and identify anomalies in real-time. Anything outside the normal baseline is flagged for swift action, helping you stay one step ahead of bad actors.

1. Protecting AI systems themselves: As AI becomes integral to finance, healthcare, government, and more, attackers now look for ways to exploit AI models directly. Threats include adversarial attacks (tricking AI into making wrong decisions) and data poisoning (tampering with the training data). Safeguarding AI from these threats ensures reliable outcomes and maintains consumer trust.

By understanding and addressing both sides, organisations can capitalise on AI’s strengths while ensuring AI systems remain secure and resilient against sophisticated threats.

Cybersecurity focuses broadly on protecting digital systems from cyber threats. AI security refers to the two specific notions outlined above:

• Leveraging AI to detect and respond to security challenges.
• Protecting AI-specific processes.

Focusing on protecting AI-specific processes, these AI models can be compromised by malicious attacks without additional protective steps.

These include attacks on machine learning algorithms, prompt injection attempts in generative AI, and data poisoning that compromises models’ training data. Let’s take a moment to explain what these AI security risks are:

• Adversarial attacks: Maliciously crafted inputs that deceive machine learning algorithms into producing incorrect or harmful outputs.
• Prompt injection attempts: Exploiting gen AI by feeding manipulated prompts or instructions, prompting the system to generate unintended or unsafe results.
• Data poisoning: Deliberately tampering with training data to alter how AI models learn, degrade performance, or produce inaccurate predictions.

Cybersecurity measures like network security and cloud identity remain vital, but AI security adds an extra specialised layer. AI’s life cycle requires special protection to ensure the output isn’t compromised, meaning you need to protect a model’s training, deployment, and ongoing monitoring.

As AI capabilities evolve, the attack surface and potential security issues likewise expand with it. This means we are going to need increased AI security expertise to keep our AI systems safe and protect sensitive information.

AI systems are now central to everyday services, from AI assistants and chatbots to advanced analytics in finance or healthcare.

Their popularity also makes them prime targets for malicious actors who want to steal data, disrupt services, or damage reputations. To circumvent this threat, AI developers are consistently working to safeguard AI systems from these threats and maintain consumer confidence.

We’ve all seen global news reports of data breaches, where customer information (such as addresses, passport numbers, or drivers license details) is hacked, ransomed, and sometimes leaked. Although these cyber attacks aren’t exclusively AI attacks, they demonstrate how even secure companies with multilayered protection can fall victim to sophisticated attacks.

Technological advancement is a double-edged sword: Even as AI systems improve, threat actors are becoming more adept at exploiting vulnerable systems, potentially impacting any AI processes that rely on cloud environments.

For example, imagine if an AI application at a major Australian bank or in a national healthcare database was specifically targeted. The consequences would be even more severe if those AI systems and their cloud security were compromised.

By focusing equally on using AI for security (faster threat detection, smarter defences) and securing AI systems (preventing adversarial attacks, safeguarding data), you can keep services running smoothly, protect customer information, and maintain trust in AI-driven solutions.

There’s also the issue of compliance. As governments catch up and implement data governance regulations to reign in the AI Wild West, AI security will play a major, mandated role in preventing bad actors from harming others.

The guiding principle here is about ensuring AI is a force for good for society through effective security measures.

The primary benefit of AI security is that it allows you to protect your product (and, by extension, the customers who use it).

See it this way: securing your AI systems helps protect the integrity of your data and the accuracy of your model outputs. If attackers manage to tamper with the data you feed your AI, your results could be skewed or unreliable.

By defending against adversarial attacks and data poisoning, you keep your models producing trustworthy results.

Strong AI security also helps you detect and respond to threats faster. Monitoring tools can spot unusual activity or new attack methods so you can take preventive action before the problem spreads.

Beyond the literal technical benefits, we’ve already mentioned the notion of consumer trust. When people know and see you’re dedicated to responsible AI and data protection, they’re more comfortable sharing their information and engaging with your AI-driven services.

Since AI technologies, products, and services often depend on customers’ willingness to share data, customer buy-in is important. It enables you to continue improving your product with personalisation thanks to their willingly given data, resulting in a positive reinforcement loop.

Of course, data protection is especially important in regulated industries like healthcare or finance, where compliance demands tight data security.

Ultimately, you need solid security practices to develop alongside your smart AI capabilities. By combining the two, you can scale with confidence, knowing you are doing all you can to prevent risks. You won’t let your customers down, and you’ll be complying with data governance laws, too.

The benefit, in this sense, is that sufficient security lets you mitigate risk as you innovate with confidence.

Think of AI security as a multi-layered safety system for both your home and the valuable tech inside. Here are four key layers:

Like a guard dog that knows your home’s regular visitors and sounds the alarm when it sees something unusual, AI “learns” what normal behaviour looks like in your environment. Any odd or out-of-place activity — such as a sudden surge in network traffic or unfamiliar login attempts — gets flagged for a closer look.

When that guard dog senses trouble, it doesn’t just bark; it can lock the doors and alert the family right away. In the AI world, automated tools can shut down suspicious accounts, isolate compromised systems, or notify security teams instantly, preventing small threats from becoming major breaches.

Imagine a factory assembly line where you want to ensure the raw materials are clean and safe from contamination. In AI, “raw materials” are your training data, and “the assembly line” is how you build and deploy your model.

Every step — from collecting data to setting up APIs — needs its own checks, like confirming data hasn’t been tampered with (no “data poisoning”) and making sure only authorised people can access the model.

Just as you’d get a tune-up for your car to keep it running smoothly, AI models need consistent check-ups. Threats evolve quickly, so it’s important to revisit your setup, fix any vulnerabilities, and retrain models if you suspect they’ve been compromised. This constant maintenance helps keep your security measures effective over time.

By combining these tactics, AI security can both spot and handle new threats — whether they’re targeting your overall systems or specifically aiming to disrupt your AI models.

We know implementing AI security can feel like another daunting obligation. But, like any intimidating task, breaking it down into manageable steps can help your organisation protect sensitive data, maintain reliable AI outputs, and keep customer trust high. In short, it’s a worthwhile endeavour, one we can tackle with a straightforward approach:

Start at the very beginning: Where do you use AI in your organisation? What data does it rely on? Which regulations (like the AI Act or data privacy laws) apply to your context? Answers to these questions will help you prioritise resources to understand where to focus your security efforts.

Your data pipelines are the journeys your data takes as you gather and use it throughout your organisation. From the moment data is gathered to when it’s fed into AI models, ensure it’s properly encrypted and protected against unauthorised access. Think of it like sealing up a water pipeline, even a small leak could cause big problems down the line.

For example, imagine you have a small glitch that exposes only a sliver of information, like partial user emails. At first, it might not look like a crisis. But if attackers catch on, they can piece together more data (or inject their own malicious data), eventually gaining deeper access or damaging your AI model’s accuracy. Even that “little” leak can snowball into a major breach or reputational nightmare down the road.

To prevent this, you can do various things:

1. Encrypt the data right when it’s collected — often with protocols like TLS (Transport Layer Security) for data in transit.
2. Once the data arrives at your storage system (such as a secure data lake), you again encrypt it at rest and enforce strict access controls.

This layered approach — encrypting data both in transit and at rest and limiting who can access it — helps keep your AI pipeline leak-proof.

We already mentioned this, but it warrants its own point. You should limit who can view and modify training data, models, and results. Tools like zero-trust networks, role-based authentication, and secure APIs can help keep sensitive information in the right hands.

Use AI-driven security tools that continuously monitor network activity and model outputs. Continuous monitoring can detect unusual behaviour (like prompt injection attempts or data poisoning), and these AI cybersecurity solutions respond faster than human teams can on their own.

This is one of those situations where AI capabilities simply outstrip humans in terms of quickly analysing and nullifying advanced threats. Setting up these automated AI security features puts your mind at ease, knowing these tools are working away at spotting anything nefarious and acting accordingly without your intervention.

AI security is more than a tech challenge. As with any organisation, your day-to-day functioning still relies on people and processes. Run workshops and training sessions to ensure your employees know how to handle emerging threats and understand the software you are using to maintain a secure operation.

Schedule frequent audits or retraining sessions, so your AI models remain reliable. This is especially important considering the fast pace at which AI is likely to keep evolving.

Taking inspiration from the implementation steps outlined above, here’s a quick rundown and reminder of some AI security best practices:

Encrypt it at the source and keep it that way while it’s in motion. This way, if somebody intercepts it, they’ll only ever see encrypted text.

Your AI tools and systems need to be on a strictly need-to-know basis. Use role-based permissions to ensure only authorised people (or services) can modify or view your AI assets. This reduces both accidental errors and intentional misuse.

Consider running adversarial tests that mimic real-world attacks, such as data poisoning or prompt injection attempts. These “fire drills” help you spot vulnerabilities and prepare your defences before actual attackers come knocking.

Anomaly detection tools (powered by AI or otherwise) watch for strange network activity and model outputs, flagging problems early. Respond quickly to any potential threat indicators like upsurges in traffic or suspicious changes in model predictions. Be proactive in your investigations.

Threats evolve, and so should your AI models. Schedule regular retraining sessions (for the model) using fresh data and address any known vulnerabilities. A stale model is an easy target for malicious actors.

This is the big-picture layer. Document how you develop and use AI. Review your ethical considerations (like bias in datasets). As you document your AI models, you’ll check against regulatory requirements to ensure you meet them. Yet, you should develop your own governance framework, too, preventing confusion and helping maintain trust across teams and with your customers.

AI security is more than another necessary box you have to tick. It’s about an ongoing, ever-present commitment to safeguarding your data, which in turn protects your brand. It’s a vehicle through which you build lasting trust with customers.

By encrypting data, controlling access, simulating threats, and keeping your models fresh, you can maintain a proactive stance against both common and emerging risks (what protects today may not cut it tomorrow).

As regulations develop and AI becomes even more deeply embedded in everyday services, businesses that prioritise AI security will stand out as responsible innovators — a reputation that wins customer loyalty and is a great foundation for growth.

Explore Agentforce to see how our comprehensive AI platform can help you uncover AI-driven insights while keeping all your critical systems secure. We’ve designed our unified platform to help you innovate quickly without compromising on safety or compliance.