Organizations can incorporate AI into cybersecurity practices in many ways. Short for artificial intelligence (AI) security, AI security is the process of using AI to enhance an organization’s security posture. Securiti’s eBook is a practical guide to HITRUST certification, covering everything from https://scale-models.net/the-risks-of-collecting-what-you-need-to-know/ choosing i1 vs r2 and scope systems to managing CAPs & planning… Discover how siloed security alerts create hidden toxic risk combinations and how correlated context helps reduce alert fatigue and uncover compound risks faster.
Organizations face unique risks like shadow AI, data exposure, prompt injection, and pipeline attacks. As businesses rapidly adopt GenAI tools and agents across complex infrastructure, traditional security programs fall short. We simplify this with CASB integrations for tools like ChatGPT and Gemini that automatically surface misconfigurations and other security risks. AI-SPM constantly manages your AI security by finding and fixing model vulnerabilities. Cloudflare secures AI with a unified platform that protects both internal workforce tools and public applications. Top AI risks include data poisoning, prompt injection, model abuse, intellectual property theft, and sensitive data exposure through AI model outputs.
Manage and govern unstructured data to enable its safe use with generative AI Establish controls for safe adoption of AI technologies including https://www.montsec.info/zero-party-data-the-structural-reset-of-privacy-and-personalization/ GenAI AI agents are autonomous systems that can call APIs, run code, and take actions without direct human intervention to achieve complex goals.
Explore AI security resources
- Our research reveals 5 plays that help CEOs execute more successfully on strategy, deliver more consistent business results, and scale AI enterprise wide.
- Identity and access management (IAM) is a cybersecurity discipline that deals with user access and resource permissions.
- AI tools can help organizations improve data protection by classifying sensitive data, monitoring data movement and preventing unauthorized access or exfiltration.
- Check Point delivers one platform to discover, protect, and govern every AI interaction from employees to agents.
- From GPU-powered inference and Kubernetes to managed databases and storage, get everything you need to build, scale, and deploy intelligent applications.
- AI security attacksHow to detect threatsBest practicesTools and solutionsWhat is an SBOM?
Learn how today’s security landscape is changing and how to navigate the challenges and tap into the resilience of generative AI. By using relevant and accurate training datasets and regularly updating AI models with new data, organizations can help ensure that their models adapt to evolving threats over time. AI can also automate patch management to reduce exposure to cyberthreats promptly.
Keep agents from taking unsafe actions
Build and host remote MCP servers on Cloudflare to expose secure, scalable agent tools. Implement AI security posture management (AI-SPM) to find and fix AI tool misconfigurations. Secure AI access to verify users and devices before they reach AI tools and resources.
Attackers typically access the model’s API and submit a large volume of carefully crafted queries to observe the model’s prediction confidence scores. Model inversion is a privacy attack where adversaries use a model’s outputs to reverse-engineer sensitive information about its training data. The technical mechanism involves injecting crafted examples, such as specific pixel patterns or biased text sequences, to introduce a hidden backdoor. A poisoned model might perform normally on the majority of inputs, but trigger a specific malicious output when presented with a rare, attacker-controlled pattern. As AI security risks increase over time and more data is used in AI models across industries, more standards have emerged to govern AI data sovereignty and use.
It aids in understanding, categorizing and safeguarding sensitive data, ensuring regulatory compliance. It provides extensive visibility and control over various devices and platforms. MaaS360®, harnessing the capabilities of AI, facilitates the management and security of enterprise devices. This approach simplifies access for verified users and reduces the cost of fraud by up to 90%. AI-powered risk analysis can produce incident summaries for high-fidelity alerts and automate incident responses, accelerating alert investigations and triage by an average of 55%.
- Part of this testing involves mimicking AI security risks, such as prompt injection, model theft, model inversion, and guardrail jailbreaking, to identify weak spots.
- For example, red team exercises—where ethical hackers behave as if they are real-world adversaries—commonly target AI systems, machine learning models and datasets that support AI and ML applications.
- As agents gain access to tools, files, and systems, security teams need guardrails that govern what agents can do, not just what they can say.
- This machine-speed collapse is difficult to contain because the chain of reasoning in these systems is often opaque to human observers.
- This approach simplifies access for verified users and reduces the cost of fraud by up to 90%.
For example, attackers can use AI to automate the discovery of system vulnerabilities or generate sophisticated phishing attacks. Securing AI projects involves practices to secure AI data, models and usage. From GPU-powered inference and Kubernetes to managed databases and storage, get everything you need to build, scale, and deploy intelligent applications. Bare Metal GPUs—dedicated, single-tenant GPU servers for large-scale training and high-performance inference DigitalOcean’s AI-Native Cloud extends that same simplicity to AI workloads, giving teams the tools to train, run inference, and deploy agents at scale without the operational overhead.
- Whether it’s helping your AI workloads run smoothly or keeping your team focused on what’s important, AI security will make your AI strategy stronger.
- Enforce zero trust policies to authenticate and authorize all requests.
- Join us live for an IBM Technology Summit focusing on Agent Ops and Responsible AI to learn IBM’s perspective on operating agentic AI responsibly at scale.
- Some attacks even use model confusion to trick developers into downloading malicious replacements for common dependencies.
- Attackers typically use gradient-based methods, like the Fast Gradient Sign Method (FGSM), to push data across the model’s decision boundary with high confidence.
- Following Veeam’s acquisition of Securiti, the launch of Agent Commander marks an important step toward helping enterprises adopt AI agents with greater confidence.
Benefits
Enforce zero trust policies to authenticate and authorize all requests. Set real-time spend limits so AI usage stays predictable, even as teams work across multiple models and providers. Combine SaaS analytics and traffic policy controls to manage risky or unsanctioned AI usage.
Solutions by industry
Those with limited AI security reported an average data breach cost of USD 5.05 million—14.9% less than those with no AI security at all. Organizations without AI security face an average data breach cost of USD 5.93 million according to the Cost of a Data Breach Report. By automating threat detection and response, AI makes it easier to prevent attacks and catch threat actors in real time. The shift to cloud and hybrid cloud environments has led to data sprawl and expanded attack surfaces while threat actors continue to find new ways to exploit vulnerabilities. All these practices also function to help businesses manage AI risk and address compliance issues. For example, red team exercises—where ethical hackers behave as if they are real-world adversaries—commonly target AI systems, machine learning models and datasets that support AI and ML applications.
This type of attack has become a primary vector for businesses, where attackers impersonate executives to authorize fraudulent transfers. These adversarial examples exploit the fact that models rely on statistical patterns rather than true semantic understanding. Evasion attacks involve making subtle, often imperceptible changes to input data to cause an AI model to misclassify or misinterpret information at inference time. This means that an organization downloading a popular model can inherit a full system compromise before any AI inference even begins. The 2026 AI supply chain includes pre-trained models, datasets, and third-party libraries that introduce hidden threats through public repositories. This machine-speed collapse is difficult to contain because the chain of reasoning in these systems is often opaque to human observers.