What is AI security?
AI security encompasses two key areas:
Using AI to defend systems through anomaly detection, log triage, and pattern recognition.
Protecting AI assets like large language models (LLMs), vector stores, and training pipelines from emerging threats.
Most organizations nowadays have woven numerous AI technologies into their fabric, and yours is likely no different. So as adoption and usage continue to rise, it’s important not just to secure your AI implementations but also to use AI-based tools to improve your overall security posture.
Get the 2025 Report on AI in the Cloud
AI adoption is exploding, but so are the risks. See what Wiz Research uncovered about DeepSeek, self-hosted models, and emerging threats in this must-read security report.
Using AI to enhance your security posture
AI-powered tools help you defend against cyber threats via various capabilities, such as behavioral analysis, automated threat detection, and predictive threat intelligence. Plus, many can perform real-time incident response to neutralize threats much faster than you could with traditional security methods. As a result, you can stay ahead of even the most sophisticated attacks, reduce false positives, and scale your defensive capabilities across complex IT environments.
The downside, however, is the proliferation of AI security vendors, each promising cutting-edge capabilities and faster, smarter protection. This surge reflects both the high priority that vendors and customers place on AI-driven security solutions and the market’s recognition of AI as a key differentiator. However, the sheer volume of new entrants and overlapping offerings only creates noise and leaves organizations to sort through a crowded field to find the right solutions for their needs.
What to look for in AI-based security tools
Traditional tools weren’t built for models that hallucinate, APIs that execute natural language commands, or data pipelines that ingest Reddit posts. If you're evaluating AI security platforms, start with this question: can it see, analyze, and defend across the entire AI lifecycle?
Genpact’s case is a great example of the benefits of using AI-based security tools. The company was able to accelerate remediation, reduce manual work and unnecessary alerts, and enhance its security posture by taking advantage of some key AI-powered features. These include the following:
Contextual risk correlation: Correlates risks across cloud workloads, LLMs, code libraries, configurations, and identities
Automated attack path detection: Identifies critical attack paths and automates remediation recommendations
Continuous AI model monitoring: Detects misconfigurations and vulnerabilities within AI models, training data, and AI services in real time
LLM and AI model discovery: Provides full visibility into deployed LLMs and AI models so exposures and vulnerabilities are far less likely to go unnoticed
Risk-based prioritization: Reduces alert fatigue and the need to manually triage low-severity or low-business-impact issues
According to Genpact's deputy chief information security officer, leveraging these AI-powered solutions ultimately helped the company “accelerate the pace of AI application development and deployment while enforcing AI security best practices. As a result, [they] can deploy AI applications that are secure by design and build trust with key stakeholders.”
You can do the same if you have a tool in your arsenal that offers the above features.
AI systems are a new attack surface
Enterprises need to defend against malicious actors especially. No matter the use case—service operations optimization, customer service chatbots, or otherwise—all AI is susceptible to cyber attacks and other vulnerabilities.
For example, many data engineers and other agile teams use generative AI solutions like LLMs to develop applications at speed and scale. And many cloud service providers, such as Azure Cognitive Services, Amazon Bedrock, and Vertex AI, offer AI services to support this development. However, they’re not as secure as you might think and require robust fortifications.
The importance of securing AI systems
AI vulnerabilities are a common vector for data breaches, and software development lifecycles (SDLCs) that incorporate AI are increasingly susceptible to vulnerabilities.
GenAI in particular poses many risks. Think of tools like WormGPT and FraudGPT, for example, which are similar to ChatGPT but with a focus on conducting criminal activity. This use of chatbots as weapons suggests that companies will soon have many unpredictable AI-related cybersecurity challenges to reckon with.
Add to this the fact that cloud environments are growing increasingly complex and, therefore, more challenging to secure, and the playing field grows even more complicated. For example, we found during research for our 2025 AI Security Readiness report that only 22% of respondents have a single-cloud architecture. 33% instead use multi-cloud setups, while an even larger percentage (45%) have a hybrid cloud setup.
Luckily, AI in cybersecurity helps you ward off various types of threats. But it’s important to remember that AI isn’t inherently secure—so it’s up to you to secure it.
AI security risks
The best way to tackle AI security is to thoroughly understand the biggest AI security risks:
Increased attack surface: Integrating AI into SDLCs fundamentally changes an enterprise's IT infrastructure, introduces many unknown risks, and broadens the attack surface. If attackers are able to exploit expanded entry points, operational disruption and even regulatory violations can result. That’s why security teams need complete visibility into AI infrastructure to remediate vulnerabilities.
Higher likelihood of data breaches and leaks: Only 24% of GenAI projects are secure—and that doesn’t even account for broader AI projects. Less emphasis on security than on adoption means a higher risk of breaches. Besides consequences like disruption, profit losses, and reputational damage, companies are also facing more pressure to comply with emerging AI governance regulations like the EU AI Act.
Chatbot credential theft: Stolen credentials from ChatGPT and other chatbots are the new hot commodity in illegal marketplaces on the dark web. For instance, there were more than 100,000 ChatGPT account compromises between 2022 and 2023, which highlights a dangerous AI security risk that's likely to increase. These breaches expose organizations to intellectual property theft—and, of course, they’re a competitive disadvantage anytime proprietary business info falls into the hands of threat actors and competitors.
Data poisoning: The Trojan Puzzle is one example of how threat actors can influence and infect datasets to choreograph malicious payloads. This type of attack—data poisoning—can lead to harmful or discriminatory outcomes that violate anti-bias regulations and increase the risk of costly litigation.
Direct prompt injections: Direct prompt injections involve threat actors deliberately designing LLM prompts to compromise or exfiltrate sensitive data. Among the risks of this type of attack are malicious code execution and sensitive data exposure.
Indirect prompt injections: Threat actors can also guide a GenAI model toward an untrusted data source to influence or manipulate its actions and payloads. Repercussions of indirect prompt injections include malicious code execution, data leaks, misinformation, and malicious information making it to end users. These attacks can also trigger compliance violations, fines, and breach notifications under data protection frameworks like GDPR and CCPA.
Hallucination abuse: AI has always been prone to hallucinating certain information, so threat actors often try to capitalize on this weakness. They do so by registering and “legitimizing” potential AI hallucinations so malicious and illegitimate datasets influence the information that end users receive. This is especially important to avoid in heavily regulated, sensitive industries like healthcare and financial services to keep operations running without interruption.
Vulnerable development pipelines: AI pipelines broaden the vulnerability spectrum, particularly in areas like data science operations that extend beyond traditional development boundaries and thus require robust security protocols to protect against breaches, IP theft, and data poisoning. To avoid software liability issues and regulatory non-compliance across the product lifecycle, it’s crucial to mitigate the supply chain risks that stem from unsecured AI development environments.
Top AI security challenges
To add to the risks above, there are many other challenges to be aware of. Below are some key findings from our 2025 AI Security Readiness report:
| Challenge | Supporting research |
|---|---|
| Lack of AI security expertise | “31% of respondents cite a lack of AI security expertise as their top challenge.” |
| Shadow AI and lack of visibility | “Shadow AI is also on the rise—25% of respondents don’t know what AI services are running in their environment, raising further concerns about visibility and governance.” |
| Reliance on traditional security tools | “While traditional security approaches like EDR and vulnerability management remain prevalent [...] only 13% of respondents have adopted AI-specific posture management.” |
If AI adoption is currently outpacing your organization’s security (as it is in so many others), it’s time for you to prioritize security initiatives.
8 AI security recommendations and best practices
Now that you know the biggest risks, let’s take a brief look at how enterprises can mitigate them. Here are eight AI security best practices that are worth implementing:
1. Use AI security frameworks and standards
Cybersecurity frameworks have long been a powerful tool for enterprises to protect themselves from rising threats. The following AI security frameworks provide a consistent set of standards and best practices to remediate security threats and vulnerabilities:
NIST’s Artificial Intelligence Risk Management Framework breaks down AI security into four primary functions: govern, map, measure, and manage.
The OWASP Top 10 for LLMs identifies and proposes standards to protect the most critical LLM vulnerabilities, such as prompt injections, supply chain vulnerabilities, and model theft.
Wiz’s PEACH framework emphasizes tenant isolation via privilege hardening, encryption hardening, authentication hardening, connectivity hardening, and hygiene (P.E.A.C.H.). Tenant isolation is a design principle that breaks down your cloud environments into granular segments with tight boundaries and stringent access controls.
Implementing any of these frameworks will require two things: First is cross-functional collaboration between security, IT, data science, and business leadership teams to ensure that your chosen framework aligns with both technical requirements and regulatory mandates. Second is clarity on the owners of each framework component so you can adapt as AI technologies and threat landscapes evolve.
AI Security Assessment Sample Report
Take a look inside a real AI security assessment—see how teams are spotting shadow AI, misconfigurations, and more.
Get Sample Report
2. Choose a tenant isolation framework and do regular reviews
While PEACH tenant isolation is specifically for cloud applications, the same principles apply to AI security. When you’re dealing with AI systems that serve multiple users or departments, you’re essentially managing a multi-tenant environment. Without proper isolation, one user’s interactions could potentially access another’s data, or a compromised AI session could spread across your entire system.
To prevent this, audit current AI user access patterns and identify where shared resources increase the risk of cross-contamination. Then, separate not just the data but also the computational resources, model access, and conversation histories between different users or business units. From there, set up automated monitoring to detect any unusual cross-tenant access attempts and put together an incident response plan for tenant boundary violations.
3. Customize your GenAI architecture
Carefully customize your GenAI architecture to ensure that all components have optimized security boundaries. Some components may need shared security boundaries, others may require dedicated boundaries, and still others may depend on various contexts.
For instance, say your financial services company is implementing a GenAI-powered customer service chatbot. You might choose to share the underlying LLMs across all customer interactions to optimize cost and performance. That shared boundary would make sense—but you’d still need dedicated boundaries for each customer’s conversation data and financial info.
To help with situations like this, create a boundary decision matrix that weighs factors like data sensitivity, regulatory requirements, performance needs, and cost implications for each AI component. Then, build it into your architecture review process and assign specific owners who are accountable for monitoring and updating boundary configurations as your AI systems scale.
4. Evaluate GenAI contours and complexities
Mapping the implications of integrating GenAI into your organization’s products, services, and processes is a must. For instance, you’ll need to make sure your AI models deliver accurate (and private) responses to end users based on legitimate datasets.
But first, look beyond the technical integration to potential ripple effects across data flows, user touchpoints, and all other places where your GenAI system could either create or amplify vulnerabilities. Also consider how your AI implementation will affect compliance requirements, user privacy expectations, and your organization’s risk tolerance. Then, conduct stakeholder interviews across departments, like legal, product, and customer service, to understand the following factors:
The most likely impacts of GenAI integration on their workflows
What technical dependencies exist
Any regulatory implications that are worth considering
Overall, doing this will give all stakeholders insight into the opportunities and risks at hand before you move ahead with deployment.
5. Ensure effective and efficient sandboxing
Sandboxing involves moving applications that incorporate GenAI into isolated test environments and putting them under the scanner.
Your sandbox needs to mirror your production environment closely enough to catch real-life vulnerabilities, but it should also be isolated enough to prevent an actual disaster if something goes wrong. Mirroring your production environment includes creating realistic scenarios to test your AI system’s boundaries. For example, you can use edge cases, malinformed inputs, and different prompt injection techniques to see how your system responds.
As details emerge on the latest threats and real-world incidents, update the test scenarios to match. Additionally, it’s worth setting up automated testing pipelines so you can run various scenarios against every AI model update and spot vulnerabilities quickly.
6. Prioritize input sanitization
Set limitations on user input in GenAI systems to mitigate AI security risks like prompt injection attacks, data leaks, and model manipulation.
A simple example is replacing textboxes with dropdown menus. But you could also use a layered approach that combines controls like character limits, keyword filtering, and format validation (such as only allowing 500 characters and blocking suspicious phrases like “ignore previous instructions” or unusual character combinations).
In any case, find a balance between robust security and a smooth end-user experience. You’ll want to include user-friendly error messages to help legitimate users, but these shouldn’t give away too much information on your security measures, either. That’s why it’s helpful to track rejected inputs and user behavior patterns—this will allow you to see where users are truly getting stuck vs. what attempts are malicious so you can adjust sanitization methods and error messages accordingly.
7. Optimize prompt handling
You’ll need a reliable way to monitor and log end-user prompts while immediately flagging malicious code execution or anything else that seems suspicious. To help with this, you could implement a prompt logging system that does the following:
Automates prompt analysis to identify potential issues based on pattern recognition
Assigns threat levels based on factors like unusual syntax or attempts to access restricted info
Escalates questionable prompts (and all associated context) for human review
Besides implementing continuous monitoring and making sure your prompt handling strategies are up-to-date, you can also use techniques like prompt pre-processing to sanitize inputs before they reach your AI models while preserving the user’s intent.
8. Don’t neglect traditional cloud-agnostic vulnerabilities
Remember that GenAI is no different from other multi-tenant applications—it can still suffer from traditional challenges like API vulnerabilities and data leaks. Here are some examples of this:
AI endpoints will still need proper authentication and rate limiting.
Data storage will still need encryption in transit and at rest.
Network connections will still need secure configurations and monitoring.
While you definitely need to combat the latest AI security challenges, don’t forget that the basics still matter. To this end, make sure your organization doesn’t neglect overarching cloud vulnerabilities in its quest to mitigate AI-specific challenges.
How Wiz uses AI to more effectively secure your AI systems
Securing AI means protecting pipelines, models, data, and interfaces, many of which live in cloud services. Wiz, the first CNAPP to fully integrate native AI security into its platform, connects the dots between these layers. It offers full visibility, risk prioritization, and detection across code, cloud, and AI assets.
Watch 12-min Wiz Demo: Cloud Risk Meets AI Awareness
See how Wiz secures cloud environments—covering everything from misconfigs and identity risks to protecting sensitive AI training data.
Watch demo now
Wiz for AI Security introduces the following capabilities:
AI security posture management: Gives security teams and AI developers visibility into their AI pipelines by identifying every resource and technology in the pipeline without agents
Data security posture management (DSPM) AI controls: Automatically detects sensitive training data and ensures that it’s secure with new, out-of-the-box controls for extending DSPM to AI
AI attack path analysis: Offers full cloud and workload context around AI pipelines so organizations can proactively remove attack paths in their environment
AI security dashboard: Provides an overview of the top AI security issues with a prioritized queue of risks so developers can quickly focus on the most critical one
Wiz is also at the forefront of research and innovation in this area as a founding member of the Coalition for Secure AI. This means that its users are able to stay up-to-date on emerging threats and quickly access new capabilities that address them.
For more on Wiz’s current capabilities, grab our AI Security Posture Assessment Sample Report to learn what types of risks the platform can detect to improve your AI pipeline visibility.
Develop AI applications securely
Learn why CISOs at the fastest growing organizations choose Wiz to secure their organization's AI infrastructure.
