From employee productivity tools to autonomous agents, there’s no escaping it: artificial intelligence is becoming part of everyday business operations.
Adopting this technology brings several benefits. At the same time, it creates new security challenges. Sensitive information can leak through prompts. AI applications are vulnerable to manipulation. Agents might gain access to tools and data beyond their intended scope.
That’s where AI security platforms make a difference. With visibility, governance, threat prevention, and more, they help businesses better control their AI systems. With so many platforms available, one big question remains: Which is best for your company? Here are the six leading AI security platforms for protecting enterprise AI in 2026.
1. Check Point
A complete AI security approach is taken by Check Point, with the platform covering three key areas: workforce AI usage, AI applications, and autonomous agents. With its unified AI Defense Plane, organizations are better positioned to tackle risks across the many places AI enters their environments. It doesn’t simply stick to one stage of adoption and leave gaps that need to be filled with other solutions
For workforce security, Check Point discovers shadow AI across browsers, SaaS platforms, desktop applications, coding assistants, and other tools. Contextual data loss prevention (DLP) and inline prompt protection let companies identify and prevent sensitive information from being inappropriately shared with AI services. Security teams can also apply policies to control which tools employees use and what information they can submit.
When dealing with risks such as malicious prompts and sensitive data exposure, Check Point also extends its approach to AI applications and agents. By combining visibility and governance with runtime enforcement, the platform stands out as a full-service option. It doesn’t treat each new tool as an isolated concern, allowing organizations to manage AI as an expanding part of their security management.
2. Palo Alto Networks
Another comprehensive solution is found with Palo Alto Networks’ Prisma AIRS. The platform secures AI applications, models, data, and agents across the AI lifecycle. It achieves this with an approach that takes discovery and blends it with risk assessment and protection. The result: security teams gain visibility into AI assets, as well as the risks they introduce.
A key area is AI runtime security. Prisma AIRS can directly inspect AI interactions to identify a wide range of threats. Its agent security capabilities also cover agent identities, permissions, tool interactions, and potentially unsafe actions.
Additionally, the platform supports AI red teaming, which tests applications and agents against adversarial scenarios before attackers exploit them. This makes Prisma AIRS relevant to companies either developing their own AI applications or moving autonomous agents from pilot projects into production.
3. Wiz
Wiz approaches AI protection through its cloud security platform, with a particular emphasis on identifying and reducing risks across AI infrastructure and development pipelines.
Its AI bill of materials (AI-BOM) gives valuable visibility into AI services, models, technologies, SDKs, and dependencies across an environment. This allows security teams to discover AI resources they might not otherwise know about, including unapproved services and exposed endpoints.
Wiz, through its Security Graph, also connects AI assets to surrounding infrastructure, identities, vulnerabilities, and sensitive data. These connections cannot be underestimated. They can reveal attack paths that isolated configuration checks might miss. For example, an exposed AI endpoint becomes more concerning when it connects to sensitive data or infrastructure with excessive permissions.
For businesses building and deploying AI in the cloud, Wiz merges AI-specific risk assessment with their wider cloud security workflows.
4. CrowdStrike
Through its Falcon platform, CrowdStrike’s security capabilities cover AI discovery, governance, and runtime protection. Its approach is useful for companies that need to see how AI tools and agents are being used across the likes of cloud environments and SaaS applications.
One of its most effective solutions is found through its AI Security Posture Management capabilities. This makes it easy to identify AI services and usage across cloud environments. Teams can then use this visibility to investigate misconfigurations, vulnerable dependencies, and unmanaged AI exposure.
Moreover, CrowdStrike offers shadow AI visibility services to reveal tools and autonomous agents operating without appropriate oversight. Its AI detection capabilities extend into AI interactions and agent activity, giving security teams a better viewpoint to investigate potentially risky behavior and enforce controls.
5. Lasso Security
From discovery and assessment to runtime protection, Lasso Security aims to secure AI applications and autonomous agents throughout their lifecycle. It is especially relevant to organizations developing custom agents which connect models with tools, APIs, databases, and internal systems.
Its AI security posture management capabilities map how teams build agents and how their components interact, helping them uncover design weaknesses before deployment. Lasso assesses these architectures against established security frameworks, including OWASP and MITRE, and checks whether they align with relevant regulations and internal policies. Another key capability is automated AI red teaming, which tests applications and agent workflows for exploitable weaknesses.
Lasso’s secure-by-design approach is valuable for businesses adopting agentic AI. Security teams gain a clearer picture of where an agent might be vulnerable, while developers can resolve those weaknesses before they reach production.
6. SentinelOne
SentinelOne’s Prompt Security governs AI usage and protects interactions between employees, developers, applications, and AI systems.
A key capability is discovering sanctioned and unsanctioned AI tools across an organization. Security teams can establish policies for how those services are used and apply controls to prevent sensitive information, such as personal data and proprietary code, from being shared inappropriately. Prompt Security can also redact sensitive information and block policy violating prompts in real time.
SentinelOne’s capabilities extend to agentic AI. This includes discovering agents and Model Context Protocol (MCP) servers, as well as governing what those agents can access or do. As AI moves beyond simply generating responses and directly interacting with business tools and systems, this is increasingly important.
For organizations that want to enable employee AI adoption while retaining practical controls over data exposure and agent activity, Prompt Security is a valid option.
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