Building Private Agentic AI Flows
Key Points
- Private agentic flows let AI agents reason, act, and keep sensitive data behind your own firewall, avoiding the privacy violations of sending information to public LLM APIs.
- In regulated fields like healthcare, finance, legal, or defense, using consumer‑facing generative AI services would breach standards such as HIPAA, making private deployment essential.
- A private agentic system is built in three layers: a foundation layer where the LLM runs on-premises or in a private cloud, an augmentation layer that retrieves data from internal knowledge bases or vector stores, and an action layer that executes calls to internal tools and APIs.
- Even when isolated behind a firewall, risks remain (e.g., data leakage during fine‑tuning), so careful architectural controls and safeguards are required.
- The talk outlines practical steps to design and implement these private agents, emphasizing that the location of the model and its supporting components, not just the model’s source, determines true privacy.
Sections
- Private Agentic AI Flows - The speaker outlines why AI agents that process sensitive data must operate behind a firewall, explains what makes an agent “private,” and provides architectural guidance and practical steps for building private, on‑premise agentic workflows.
- Securing Private Data in Fine‑Tuned LLMs - The passage discusses privacy risks of embedding personal data in fine‑tuned language models—such as potential extraction, regulatory compliance challenges, and insider threats—and advises anonymizing training data, enforcing strict access controls with comprehensive logging, and applying data minimization to mitigate these issues.
Full Transcript
# Building Private Agentic AI Flows **Source:** [https://www.youtube.com/watch?v=-Tz_FWVYgnM](https://www.youtube.com/watch?v=-Tz_FWVYgnM) **Duration:** 00:06:51 ## Summary - Private agentic flows let AI agents reason, act, and keep sensitive data behind your own firewall, avoiding the privacy violations of sending information to public LLM APIs. - In regulated fields like healthcare, finance, legal, or defense, using consumer‑facing generative AI services would breach standards such as HIPAA, making private deployment essential. - A private agentic system is built in three layers: a foundation layer where the LLM runs on-premises or in a private cloud, an augmentation layer that retrieves data from internal knowledge bases or vector stores, and an action layer that executes calls to internal tools and APIs. - Even when isolated behind a firewall, risks remain (e.g., data leakage during fine‑tuning), so careful architectural controls and safeguards are required. - The talk outlines practical steps to design and implement these private agents, emphasizing that the location of the model and its supporting components, not just the model’s source, determines true privacy. ## Sections - [00:00:00](https://www.youtube.com/watch?v=-Tz_FWVYgnM&t=0s) **Private Agentic AI Flows** - The speaker outlines why AI agents that process sensitive data must operate behind a firewall, explains what makes an agent “private,” and provides architectural guidance and practical steps for building private, on‑premise agentic workflows. - [00:03:13](https://www.youtube.com/watch?v=-Tz_FWVYgnM&t=193s) **Securing Private Data in Fine‑Tuned LLMs** - The passage discusses privacy risks of embedding personal data in fine‑tuned language models—such as potential extraction, regulatory compliance challenges, and insider threats—and advises anonymizing training data, enforcing strict access controls with comprehensive logging, and applying data minimization to mitigate these issues. ## Full Transcript
Let's talk about building private agentic flows. Now, these aren't just chatbots. These are agents
that can reason, take action and still keep your data completely private. Let's explore what
private actually means and why it's incredibly important for building with AI. So let's imagine
we are healthcare developers, right? We wanna use AI to summarize medical docs or craft an email
based on a patient's recent visit. We can take those docs and we can take our notes, and we can
send them to a consumer-facing web application that fronts one of the big gen AI platforms and
get back really decent results. Now, obviously do not do this, but there is
another huge problem besides the obvious. You'd be sending protected healthcare info to a third
party without proper safeguards, violating HIPAA. And you really don't want to do that.
And if you're in finance, legal or defense, just like healthcare, these public API endpoints are
usually off the table. Agentic AI is incredible, but if you're working with sensitive data,
consumer-facing LLMs and public APIs are a deal breaker due to the privacy concerns. So, how
exactly do we use the power of agentic AI in these applications? The answer is private agentic
flows where the agents that act on your behalf are keeping your data behind your firewall. In the
next few minutes, I will tell you what makes an agent private. Some of the architecture behind
these systems, and maybe a few practical steps to start building your own private agents. Let's
first quickly go over what we mean by agentic AI. The LLMs of the past, and realistically, that's
like last year, were fundamentally reactive and relatively simple. You ask a question and they
respond. But now we have agents and they can reason. They can act on your
behalf. They can do all these complex tasks. But if you're still using
LLMs that are connected to the public cloud, you cannot work with
sensitive data. So what is a private agentic flow? Let's think of it in three layers. Our
first layer is gonna be the foundation layer. And this is where our LLMs would run, right? It
doesn't matter if it's closed source or open source. It just matters where they run. Your model
must run entirely on your infrastructure, whether on prem or in your private cloud environment. Then
there's the augmentation layer. And this is where we can have our RAG or we could have our VectorDB
or we can have our fine-tuned adapters. Your agent retrieves info from your private knowledge
bases or your document repositories to ground its responses with your specific and private data.
And finally, we have our action layer, right? Now this is where the tools and the APIs that your
agent needs to use in order to function will live. So, when it makes a call to your DB or when it
does anything or creates any kind of information that it needs for you, this is where it's gonna
live. But just because we're behind a firewall does not remove all potential risks.
Right? Think about when you fine tune an LLM and you're using private data. That information is
embedded into your model. So even though your model is private and behind your firewall, that
data that's inside there, if someone gets access, they could potentially extract it. Next, and this
is a big one, is regulations, right? GDPR, HIPAA they each have different requirements.
Compliance with requests to remove personal data is a pretty complex task when data is embedded in
a model. There are some techniques that can extract it and remove it, but they are imperfect
and they are still evolving. And finally, we have insider threats, right? And it's not
necessarily that someone's trying to do something bad, but they might misuse the system or
accidentally expose data. So how can we resolve this? First,
the data that we're training the model on, you can anonymize it, right? That's big. You just scrub any
PII before it ever touches your LLM. Replace names with tokens or hashes. Remove identifiers. Strip
out anything that could trace back to a real person. Make your data anonymous from the get go.
Second, access control. Implement strong access controls. Not every single
person needs ta to touch your system, right? Log every prompt. Log every
interaction, every query, every retrieval. This will create compliance trails and ensure only those
who absolutely need to touch the data can touch the data. And finally, data minimization. Only give
your agents access to the minimum data they need for their specific tasks. Don't give your
appointment scheduling agent access to full medical histories when it only needs the patients
names and when they're when they're available. So this isn't just theoretical. Devs are building
these private agents in the most regulated industries on earth. Like we mentioned before,
healthcare, right? Developers in healthcare are building agents to help doctors summarize patient
histories, craft emails, track statuses. The agent retrieves data from electronic health records and
references medical research. But the data never leaves the network. And then we have
financial services. Teams at banks are building their agents for fraud detection customer service
where they analyze transactions, flag anomalies, use customer data, and that all must stay within
their secure infrastructure. And then we have legal.
Developers at law firms are building agents to search case law, draft contracts and identify
relevant precedents from private case databases. And once again, it stays within their network.
And finally, and probably the most regulated, is gov and defense, right? If you're
working with intelligence or defense, you need these agents to analyze classified documents or
connect dots across data sources. And consumer LLM services aren't even considered. Private agentic
systems are the only option here. Now, these developers and teams know the value of agents, and
they had to find a way to implement them. Private agentic flows are a necessity if you're
building applications that handle sensitive data. And as AI becomes more integrated into sensitive
and critical workflows, the questions will not be should we go private? But rather, how quickly can
we get there?