PlainID
7.4K posts

PlainID
@plainID_authZ
PlainID is the Agentic Identity Platform at the core of your identity stack, making real-time decisions for every human & non-human identities.
Katılım Nisan 2016
667 Takip Edilen449 Takipçiler

If you're deploying AI agents that can retrieve data, invoke tools, and operate across systems, what boundaries are you enforcing?
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If your AI agent is allowed to query customer data and allowed to send emails, how do you stop it from combining those two in ways you never intended?
The short answer is, you can't. Not with static authorization.
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“It’s fine, it’s internal data.” “It’s our private AI.” "It's just for ourselves, what bad could it do, right?"
Wrong. If your internal AI agent has broad standing access to sensitive systems, it is effectively an insider with superhuman speed and scale.
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We should stop assuming that AI agents operating with broad, standing permissions are autonomous. They're just unsupervised.
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For years, RBAC was “good enough” because most systems were predictable. Humans had job functions, applications had workflows, and permissions could be approximated with roles and exceptions.
That era is ending.
Gal joined @IDACPodcast to explain: hubs.ly/Q0468JgR0
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If you think AI security is the SecOps team kinda thing, think again. Cause every single team can build their own AI agent today. Will your SecOps team even know about this? We doubt that.
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If Gartner predicts that over 50% of successful cybersecurity attacks against AI agents will exploit access control issues by 2029... then that number should stop you.
If you’re deploying agents and want to do it responsibly, early access is open: hubs.ly/Q045wkK50
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Authorization is a security decision.
Listen to the full conversation at @IDACPodcast here: hubs.ly/Q045wr4-0
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If access is too broad, agents will use it.
If data is reachable, it can enter the pipeline.
If tools are callable, they expand your attack surface.
That is why we built a new PlainID platform to govern the full agentic AI: hubs.ly/Q045wsMS0
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Agents are built to be helpful. So the control can't live inside the agent, and definitely not as a polite prompt, "please don't." It should live outside the agent as enforcement instead, "you literally can't."
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Wanna know why runtime authorization is the critical missing layer in AI security, and why @Gartner_inc, @cloudsa and @owasp all point to access control as the primary failure point in agentic systems? Go here: hubs.ly/Q045wkf00

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As organizations deploy agentic AI across multiple teams, there's one question that keeps surfacing: who is governing what these agents can actually do?
And because most of these AI tools focus on productivity... We built Daia to focus on control.
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Early access is now open for PlainID’s new Agentic Identity Platform.
If you are building or deploying AI agents, don't forget that authentication is not enough.
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Your biggest risk in AI is not building an inaccurate model. It’s what the model can access.
If you are deploying AI agents and want real governance built into the flow, not layered on after, request early access here: hubs.ly/Q045wvpF0
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Static permissions and traditional RBAC models were built for humans clicking through workflows. Agentic AI operates at machine speed, across multiple systems, with autonomy.
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When data acts on its own... who governs the risk?
That's exactly what Chris Hoff, our CRO, is discussing this Wednesday at @Gartner_inc Data & Analytics 2026.
We'll also be there at booth 1033, and you can book some time with us here: hubs.ly/Q045wDTm0

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Meet Daia. She's not your regular AI assistant. We like to think of her as an AI Guardian 😎
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Why telling an AI agent "not to access sensitive data" is meaningless? Cause you wouldn't ask a child to not eat the sweets you put in front of them, would you?
Cause AI agents are built to be helpful.
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Next week, we’re heading to @Gartner_inc Data & Analytics 2026! And for the first time ever, we’ll be showcasing our new Agentic Identity Platform designed to secure how AI agents interact with enterprise data, tools, and systems
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