What Is AI Security, and Why Is It Important?

“IT and security leaders are walking on a tightrope — on one side, they must protect the organisation from everything from ransomware to data poisoning, and on the other, they’re under pressure to fuel innovation in an era when AI and automation are integral to staying competitive”. — State of IT: Security (Fourth Edition), p. 2

AI is a revolutionary tool, but getting the most out of the opportunities it can deliver means developing it responsibly and keeping it safe from threats. As innovation accelerates, so do the risks.

AI security provides businesses a path towards protecting artificial intelligence systems from cyber threats and malicious individuals. In this guide, we’ll explain how it works, why it matters and how you can implement it to keep your AI operations secure.

Much of the data in this guide comes from research conducted by Salesforce in the State of IT: Security report (Fourth Edition), which reveals insights and trends from 2,000-plus security, privacy and compliance leaders worldwide.

AI security is the process of protecting artificial intelligence systems from malicious attacks, often using AI itself to bolster these cybersecurity defences.

In short, it 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.
2. Protecting AI systems themselves: Attackers now look for ways to exploit AI algorithms and models directly. Threats include adversarial attacks (tricking AI into making wrong decisions) and data poisoning (tampering with the training data).

In this guide, we’ll focus primarily on protecting AI systems from threats, while also touching on how AI itself can help with your security efforts.

Much like our traditional IT systems need to be protected from ransomware, viruses and insider threats, our AI systems also need security measures to remain operational, trustworthy and protected from exposure.

To help with this, AI security aims to protect a few key elements:

• AI systems themselves: At the top level, AI security safeguards AI models and algorithms from unauthorised access, cyber attacks and tampering.
• AI data: AI security also looks to protect the sensitive information that AI relies on from breaches, misuse and leaks.
• AI infrastructure: An AI security posture is only as strong as its weakest link. AI security also ensures the infrastructure supporting AI is resilient to attacks.

In most cases, keeping these systems secure also means utilising AI itself to improve threat detection. 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, helping you stay one step ahead of bad actors.

Cybersecurity focuses broadly on protecting digital systems from cyber threats. AI security is much more specific, focusing exclusively on keeping AI systems safe from malicious attacks.

Why is the distinction needed? Consider that 79% of security leaders believe AI agents introduce new security challenges. It isn’t enough to rehash existing cybersecurity methods to fit a new brief. AI requires its own solutions, ideas, and best practices. AI security risks include:

• Adversarial attacks: Maliciously crafted inputs that deceive machine learning algorithms into producing incorrect or harmful outputs
• Prompt injection attempts: Exploiting generative AI by feeding manipulated prompts that cause 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 operations 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.

The good news is that artificial intelligence can offer a solution to this problem, with 80% of those we surveyed agreeing that AI offers new security opportunities. So, while the attack surface and potential security issues will continue to expand as AI capabilities evolve, so too will the capabilities of AI security to keep our 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. Unfortunately, this popularity also makes them prime targets for those who want to steal data, disrupt services or damage reputations.

We’ve all seen global news reports of data breaches, where customer information (such as addresses, passport numbers or driver’s license details) is hacked, ransomed and sometimes leaked. Even secure companies with multi-layered protection can fall victim to sophisticated attacks.

These kinds of breaches do little for public trust. Our survey revealed that 64% of customers believe companies are reckless with their data, and 61% say AI advancements make data protection more important than ever. The onus is on businesses to prove they’re taking AI security seriously and won’t let innovation get in the way of AI model integrity.

With this in mind, 75% of organisations anticipate security budget increases to address everything from data poisoning to more advanced threat detection. Aside from keeping customers on side, this is also a response to the growing sophistication of attacks. 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 were specifically targeted. The consequences would be even more severe if those AI systems and their cloud security were compromised.

There’s also the issue of compliance, with 68% of security leaders in our survey saying compliance is now more difficult due to evolving legislation and 43% reporting they don’t feel prepared for potential regulatory changes associated with AI. As governments catch up and implement data governance regulations to rein in the AI Wild West, AI security solutions will play a major, mandated role in preventing bad actors from harming others.

By focusing equally on using AI for security (faster AI 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.

We’ve discussed the necessity of protecting AI systems from a risk standpoint. Now, let’s flip the script and look at some of the advantages AI security can bring to your organisation. Here are five benefits to consider:

• Security: Attacks on AI systems are becoming increasingly sophisticated. The primary benefit of AI security is that it allows you to stay up to date with these threats to protect your product (and, by extension, the customers who use it).
• AI reliability: 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 ensure your models keep producing trustworthy results.
• AI in threat detection: 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.
• Customer buy-in: When people know you’re dedicated to responsible AI, they’re more likely to engage with your AI-driven services. You can then use this willingly given data to improve your product, resulting in a positive reinforcement loop.
• Compliance: Of course, data protection is especially important for compliance in regulated industries like healthcare or finance, where compliance demands tight data security .

By combining your smart AI capabilities with robust AI security, you can scale with confidence, knowing you won’t let your customers down, are compliant with data and AI governance laws, and are doing all you can to mitigate risk.

A telling 75% of security leaders believe AI-driven threats will soon outpace traditional defences. As such, many businesses are beginning to experiment with AI-powered security to keep their artificial intelligence systems protected.

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

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 AI 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 you 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 collected 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 ‘minor’ 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 human capabilities in terms of quickly analysing and nullifying advanced threats. Setting up these automated AI security systems 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’re 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 conducting adversarial testing, which mimics 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.

You should develop your own governance and AI risk management framework, too, which will prevent confusion and help you maintain trust across teams and with your customers.

And above all, remember to be open and honest. When we asked customers what would increase their trust in AI, 42% said transparency in how AI is used would, and 31% said explainability would . If you can show the steps you’re taking to use AI responsibly, you’ll put yourself in good standing to innovate and scale without risking customer confidence.

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 and builds 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 your data today may not cut it tomorrow).

It’s also important to gain confidence in your own AI outputs. Currently, only 43% of security leaders are fully confident in the explainability of their AI efforts. If you can articulate how your AI systems work in simple terms, you’ll put yourself in a good place to build rock-solid trust with your customers.

Ready to learn more? Explore Agentforce to see how our comprehensive AI platform can help you uncover AI-driven insights while keeping all your critical systems secure. Or, read our State of IT: Security report (Fourth Edition) to discover the security concerns, risks and opportunities that are taking shape in the age of AI.