{"id":3588,"date":"2026-03-10T13:16:27","date_gmt":"2026-03-10T13:16:27","guid":{"rendered":"https:\/\/rssfeedtelegrambot.bnaya.co.il\/index.php\/2026\/03\/10\/whats-holding-back-ai-agents-its-still-security\/"},"modified":"2026-03-10T13:16:27","modified_gmt":"2026-03-10T13:16:27","slug":"whats-holding-back-ai-agents-its-still-security","status":"publish","type":"post","link":"https:\/\/rssfeedtelegrambot.bnaya.co.il\/index.php\/2026\/03\/10\/whats-holding-back-ai-agents-its-still-security\/","title":{"rendered":"What\u2019s Holding Back AI Agents? It\u2019s Still Security"},"content":{"rendered":"

It\u2019s hard to find a team today that isn\u2019t talking about agents. For most organizations, this isn\u2019t a \u201csomeday\u201d project anymore. Building agents is a strategic priority for 95% of respondents that we surveyed across the globe with 800+ developers and decision makers in our latest State of Agentic AI research<\/a>. The shift is happening fast: agent adoption has moved beyond experiments and demos into something closer to early operational maturity. 60% of organizations already report having AI agents in production, though a third of those remain in early stages.\u00a0<\/p>\n

Agent adoption today is driven by a pragmatic focus on productivity, efficiency, and operational transformation, not revenue growth or cost reduction. Early adoption is concentrated in internal, productivity-focused use cases, especially across software, infrastructure, and operations. The feedback loops are fast, and the risks are easier to control.\u00a0<\/p>\n

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So what\u2019s holding back agent scaling? Friction shows up and nearly all roads lead to the same place: AI agent security.\u00a0<\/p>\n

AI agent security isn\u2019t one issue it\u2019s the constraint<\/h2>\n

When teams talk about what\u2019s holding them back, AI agent security rises to the top. In the same survey, 40% of respondents cite security as their top blocker when building agents. The reason it hits so hard is that it\u2019s not confined to a single layer of the stack. It shows up everywhere, and it compounds as deployments grow.<\/p>\n

For starters, when it comes to infrastructure, as organizations expand agent deployments, teams emphasize the need for secure sandboxing<\/a> and runtime isolation, even for internal agents.<\/p>\n

At the operations layer, complexity becomes a security problem. Once you have more tools, more integrations, and more orchestration logic, it gets harder to see what\u2019s happening end-to-end and harder to control it. Our latest research data<\/a> reflects that sprawl: over a third of respondents report challenges coordinating multiple tools, and a comparable share say integrations introduce security or compliance risk. That\u2019s a classic pattern: operational complexity creates blind spots, and blind spots become exposure.<\/p>\n

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45% of organizations say the biggest challenge is ensuring tools are secure, trusted, and enterprise-ready.<\/p>\n<\/div>\n

And at the governance layer, enterprises want something simple: consistency. They want guardrails, policy enforcement, and auditability that work across teams and workflows. But current tooling isn\u2019t meeting that bar yet. In fact, 45% of organizations say the biggest challenge is ensuring tools are secure, trusted, and enterprise-ready. That\u2019s not a minor complaint: it\u2019s the difference between \u201cwe can try this\u201d and \u201cwe can scale this.\u201d<\/p>\n

MCP is popular but not ready for enterprise<\/h2>\n

Many teams are adopting Model Context Protocol<\/a> (MCP) because it gives agents a standardized way to connect to tools, data, and external systems, making agents more useful and customized.\u00a0 Among respondents further along in their agent journey,\u00a0 85% say they\u2019re familiar with MCP and two-thirds say they actively use it across personal and professional projects.\u00a0<\/p>\n

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Research data suggests that most teams are operating in what could be described as \u201cleap-of-faith mode\u201d when it comes to MCP, adopting the protocol without security guarantees and operational controls they would demand from mature enterprise infrastructure.<\/p>\n<\/div>\n

But the security story hasn\u2019t caught up yet. Teams adopt MCP because it works, but they do so without the security guarantees and operational controls they would expect from mature enterprise infrastructure. For teams earlier in their agentic journey: 46% of them identify\u00a0 security and compliance as the top challenge with MCP.<\/p>\n

Organizations are increasingly watching for threats like prompt injection and tool poisoning, along with the more foundational issues of access control, credentials, and authentication. The immaturity and security challenges of current MCP tooling make for a fragile foundation at this stage of agentic adoption.<\/p>\n

Conclusion and recommendations<\/h2>\n

Ai agent security is what sets the speed limit for agentic AI in the enterprise. Organizations aren\u2019t lacking interest, they\u2019re lacking confidence that today\u2019s tooling is enterprise-ready, that access controls can be enforced reliably, and that agents can be kept safely isolated from sensitive systems.\u00a0\u00a0<\/p>\n

The path forward is clear. Unlocking agents\u2019 full potential will require new platforms<\/a> built for enterprise scale, with secure-by-default foundations, strong governance, and policy enforcement that\u2019s integrated, not bolted on.<\/p>\n

Download the full Agentic AI report<\/a> for more insights and recommendations on how to scale agents for enterprise.\u00a0<\/p>\n

Join us on March 25, 2026, for a webinar<\/a> where we\u2019ll walk through the key findings and the strategies that can help you prioritize what comes next.<\/p>\n

Learn more:<\/strong><\/p>\n