Exploring thebenefits of secure,
AI-ready data
It’s time to bring yourdata into the future.

Exploring the benefits of secure, AI-ready data

INTRODUCTION
In the past few years, we’ve heard all about AI and its promised benefits, largely
around increasing productivity and replacing menial tasks. But the power of AI isgrowing rapidly and organizations are already using it in a wide range of industries,
including healthcare, finance, transportation, and technology, among others. In 2022,
the global AI market was valued at $428 billion. It’s projected to surge to $2 trillion by2030. If this seems outrageous, consider the growth of Open AI and ChatGPT in undera year. According to OpenAI, ChatGPT gained over one million users in just five days
after its November 2022 launch. AI is here to stay and it’s no longer a future technology,
as tech companies are announcing new AI products and integrations every day.

What are the benefits of AI
to large global enterprises?
At Cohesity, we’re looking at AI througha data security and data management
lens. And that’s really where we see
AI as a game changer. For example, wecan secure your data with AI-powered
early threat detection so you can get
more value from your backed up data by
exposing zero cost clones to your AI andML workflows—whether your data sits ina data center, or a public or private cloud.

2 | INTRODUCTION

AI can help you differentiate yourbusiness, driving growth andimproving customer outcomes.
In this eBook, we dive into someemerging AI use cases and explore
how we can make your data AI readywhile keeping it secure.

Exploring the benefits of secure, AI-ready dataDATA AND AI
Data is an essential aspect of AI. It’s the fuel that provides context to AI
algorithms and vectors. But AI models are only as good as the data they’retrained on, and the quality of this training data can significantly impact thevalidity, effectiveness, and power of the model. If the data available to AI models
is biased or inaccurate, the model may produce biased or inaccurate results.
AI models also require ongoing access to new data tocontinue learning and improving over time. This is why
data is so important to AI’s development and deployment.
Organizations that invest in collecting, storing, and analyzing
high-quality data are better positioned to use AI’s power
to gain a competitive advantage.
In today’s modern, distributed architecture, though, collecting,collating, and leveraging data from workflows across anorganization’s data estate can be complex. Organizations

are generally running infrastructure in a variety of locations,spanning private data centers, single or multiple clouds, SaaS
applications hosted by other organizations, and edge locations
like stores, IoT devices, and more.
They’re routinely storing petabytes (or more) of data withoutclassifying, indexing, or tracking it. This unstructured data isoften referred to as “dark data,” which Gartner defines as“the information assets organizations collect, process andstore during regular business activities, but generally failto use for other purposes (for example, analytics, business
relationships and direct monetizing).”

By most analyst estimates, unstructured
data makes up 80-90% of total data.
This dark data represents a missed opportunity.
Organizations can’t gain insights and make informed
decisions, dramatically reduce their data costs,or secure and protect this data—because they
don’t even know it exists.

3 | DATA AND AI

Exploring the benefits of secure, AI-ready data

RETRIEVAL
AUGMENTEDGENERATION (RAG)
In the era of AI, off-the-shelf trained large language
models (LLMs) have emerged as a powerful tool for
generating human-like responses. But most existingknowledge-grounded conversation models rely on
out-of-date materials. They’re limited in their ability
to generate knowledgeable responses that could
involve proprietary or domain-specific information.
The introduction of retrieval augmented generation (RAG)
models can overcome this challenge. RAG models combine thestrengths of LLMs with the ability to retrieve information frommultiple sources. RAG not only enables LLMs to generate more
knowledgeable, diverse, and relevant responses, but also offersa more efficient approach to fine-tuning these models.

4 | RETRIEVAL AUGMENTED GENERATION (RAG)

Exploring the benefits of secure, AI-ready data

RETRIEVAL AUGMENTED
GENERATION (CONTINUED)At Cohesity, we’re working on providing robust anddomain-specific context to RAG-driven AI systems
through our patented SnapTree and SpanFSarchitectures. By leveraging this robust file system,
we’re making our platform ‘AI ready’ for RAG-assisted
LLMs through an on-demand index of embeddingsthat will be provided just-in-time to the AI application
requesting the data. We also secure the data throughour role-based access control (RBAC) models.
Our patent-pending retrieval-augmented response
generation technology currently under development
accepts a user- or machine-driven input—such as aquestion, or a query. That input is then used to filter
the petabytes of an enterprise’s backup data to filter
down to a smaller subset of data. It then selects
representations from within those documents orobjects that are most relevant to the user ormachine query. That result is packaged, alongwith the original query, to the LLM to provide acontext-aware answer. This innovative approachensures that the generated responses are relevantto the enterprise’s domain-specific content.
Innovative RAG-driven AI systems like ours will present
a unique opportunity for technology and businessexecutives to leverage the power of data-driven insights
and enhance the quality of conversations across variousplatforms. Harnessing the power of our data protection
and data management capabilities, enhanced by AI,
helps organizations unlock new levels of efficiency,innovation, and growth.

5 | RETRIEVAL AUGMENTED GENERATION (RAG)

Of course, before you do anything with your data,
you need to secure it. Let’s look at the first of two
use cases: data security.

Exploring the benefits of secure, AI-ready data

USE CASE 1: USING AI TO
BOOST DATA SECURITY
Anomaly detection with AI
We help customers back up their entire data estate
and unlock new ways to protect and manage data,in addition to improving cyber resilience with dataisolation, threat detection, and data classification.
While we already provide deep insights and analytics
that improve security posture—thanks to our unique
distributed file system—soon we’ll fully leverage thesame data we’re already securing and managing using
AI models. Our new collaboration with Azure OpenAI
makes this possible.
We recently previewed how Azure OpenAI may be
integrated into the Cohesity platform to streamline
anomaly detection, provide human-readable analysesof threats, and help organizations reduce recoverytime. We demonstrated how we could take ourfoundation of log and system data, combined withinsights built into our Cohesity DataHawk threat
intelligence solution, to use AI to query all dataand generate interactive reports for CISOs andpractitioners alike.
Using Azure OpenAI, we generated an Insights
Summary that found objects on virtual machinesthat could potentially be affected by ransomware.
We used this information to explore how a CISO
might assess business impact, and how
practitioners could streamline responses.

6 | USE CASE 1: USING AI TO BOOST DATA SECURITY

During this demonstration, the integrated AI
uncovered numerous affected, highly sensitive
files showing behavior or changes associatedwith a high confidence rating for anomalies.
If a CISO were to ask what files were impacted
on the VMs, the executive-level summary output
could provide a near real-time assessment ofthe risk profile of these anomalies. Immediately,
the CISO could assess business risk while teammembers in the SOC could get details to stop,
mitigate, and respond to the threat.
From there, you could ask for more details on
the impacted systems, and the integrated AI
could offer human-readable impact analyses.
Using comprehensive metadata from the backup
and recovery files, you could view contextual
threat response information.
You can imagine how quickly cross-functional
teams could use these conversational analyses
to assess risk, align on impact, and kick off anaction plan before attackers could further
disrupt the business.

Exploring the benefits of secure, AI-ready data

USE CASE 2: GETTING MOREFROM STORED DATA
So how do you gain even more insights from all this data you’resecuring? We spent years building AI into several of our products
to help detect threats, classify large volumes of unstructured data,and protect critical data and workflows. The Cohesity Data Cloud,our modern data security and management platform, is uniquein that it is “AI ready.” It’s architected to be easily searchable, with
granular access controls. With global search, you can search acrossmultiple workloads and histories of snapshots. Future integrations
with AI and LLMs will be able to quickly answer critical businessquestions, while ensuring that only the right people see
responses regarding the data they have access to.
In the same way backup data is stored and can be searched for threat analysis,
it’s also AI ready and contains metadata that can be used with LLMs. When a person
asks questions about the data through the LLM, the model provides human-readable
responses. Using authoritative data sources backed up on Cohesity can help ensure
more accurate, actionable responses to user or machine queries.
Since we index all backup data, APIs will return context-aware responses
in a highly performant way that doesn’t use up too much compute power.

7 | USE CASE 2: GETTING MORE FROM STORED DATA

Exploring the benefits of secure, AI-ready data

GETTING MORE FROM STORED DATA(CONTINUED)

Indexing and instant search with Cohesity
We’ll also take a differentiated approach to storing andindexing massive amounts of data, enabling organizations
like yours to instantly search across your entire data estate.
With Cohesity instant search, you can quickly search for andlocate specific data and find the information you need. You
don’t have to manually search through large amounts of data.
Think of it this way: Say you’re a lawyer and you want toresearch information from a case your firm litigated years
ago. You could pull out boxes and dig through them to findthe brief in question, like a needle in a haystack. Soon, youcould simply use an AI chatbot to query the data that
already lives on Cohesity.

With our holistic view of data over time, you
can search decades-old data, instantly, andsee different variations of it during different
periods. Other companies store data without
indexing file and object metadata and have
limited history available (only days) forsearching or changes.
Thanks to the Cohesity Data Cloud,your data is AI ready.

8 | USE CASE 2: GETTING MORE FROM STORED DATA

Exploring the benefits of secure, AI-ready data

GETTING MORE FROM STORED DATA(CONTINUED)

The Cohesity Data Cloud offers:• Data aggregation and unification: We aggregate data from different sources anddata types, including on-premises, cloud, and edge locations. This makes it easier
to analyze and grant secure access to data for AI applications and identify patterns,
trends, and anomalies that may not be visible in siloed data, while also dramaticallyreducing or eliminating dark data. Using backup data that’s indexed and aggregatedallows you to leverage AI in a highly performant way.
• Data optimization: We efficiently deduplicate and store data in compact structures
which can be fortified with appropriate metadata that makes search more robust.
• Data protection: We provide enterprise-grade backup, recovery, and disasterrecovery capabilities—including a way to isolate data in a virtual air-gappedenvironment so a known clean copy can be retrieved and restored in the event ofa cyberattack. Organizations can get back up and running quickly using instant mass
restore (IMR) to instantly recover thousands of VMs, databases, files and other data.
This data is available, resilient, and recoverable when needed—which is critical for
AI applications that rely on large volumes of data.
• Data security: Our comprehensive data security proactively detects threats within
data to identify anomalies, like possible malware, using security threat feeds. Data
classification can identify where sensitive and critical data sits, and cyber vaulting
ensures if an attack happens the blast radius is mitigated. AI applications continue
running to help ensure that business operations don’t stop, even in the event of
an attack or disaster.
• Data access: Our granular role-based access controls (RBAC) for backup data helps
prevent users from accessing data they don’t have permissions for, like sensitive
data (patient data/PII, trade secrets, financials, and more). This approach applies
to AI, where the AI model only queries data and provides responses that align tousers’ permissions.

9 | USE CASE 2: GETTING MORE FROM STORED DATA

Exploring the benefits of secure, AI-ready data

Using the power of AI,
we offer countless opportunities fororganizations like yours to unlock thepower of your entire data picture overtime. You’ll have AI-ready data that’s
resilient and available when needed.

9 | USE CASE 2: GETTING MORE FROM STORED DATA

Exploring the benefits of secure, AI-ready dataCONCLUSION
AI is poised to be the next big disruptor in today’s macro businessenvironment—from streamlining operational processes to transforming
customer experience via new data insights-driven services. And that’s
just a start. At Cohesity, we’re focusing on the power of AI to improvedata protection and security via advanced threat and anomaly detection.
We create AI-powered backups that are fully hydrated and ready fornearly instant zero cost cloning, and safe from harm, too—turning
data backups into treasure troves of data insights.
Glossary:
Retrieval augmented generation (RAG) isa NLP technique that combines the benefitsof retrieval-based and generative-based
approaches to improve the quality of text
generation tasks, such as question-answering,summarization, and conversational AI.
Generative AI uses algorithms to generate
new content (written content, images, video,audio, and computer code, etc…) basedon user input. Unlike earlier versions ofAI, generative AI can create new content,
like news articles, poetry, or cyber threat
analyses presented in a conversational UI.

10 | CONCLUSION AND GLOSSARY

Conversational AI simulates human conversations
through natural language processing (NLP) and related
techniques such as transformers. It can interpret user
input and generate appropriate responses based onan understanding of the user’s intent.
Cognitive AI aims to replicate human cognitive abilities
such as perception, reasoning, problem-solving, anddecision-making. It uses ML, NLP, and related techniques
to create intelligent systems that can learn and improvebased on interactions with users and the environment.
Retrieval AI involves searching and retrieving
information from large datasets. It uses
techniques such as NLP and ML algorithms
to understand the intent of a user’s queryand retrieve the most relevant resultsfrom a database.

©2023 Cohesity, Inc. All rights reserved.

6100021-001 EN 5-2023