EvidentiaSIHFinalDemo

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This is a demonstration video showcasing our product Evidentia,

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which is a legal document management system that comes with a suite of security

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features.

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It includes role -based access at the front -end dashboard level secured by Django

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and at the back -end secured by Spring Security and JWT.

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It also has several other features called the Evidentia AI Mind,

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which is another feature I will talk about.

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The main important feature that we are going to talk about here is pre -upload

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tamper detection, which is the most interesting feature of our product.

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It is something that has not been addressed in research papers relating

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to chain of custody and blockchain security,

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which we were able to address with our product.

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So just to quickly get into how our product works,

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we have a Spring Boot service that secures the backend.

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And this is entirely written in Java.

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Spring Boot is a Java backend management framework.

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That is a part of the Spring framework.

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I'm just going to run this quickly.

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And this gives you role -based access through Spring Security.

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Now we have more terminal windows here.

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If you can see, this is the dashboard,

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the Django dashboard window,

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which is responsible for rendering our front end.

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It is also secured with the Django auth.

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And that is why you see credential systems here.

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I'm going to run the front end as well.

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This is a live database that is written in Postgres.

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By the way, all of this runs locally on your local machine.

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You don't have to worry about security because all of this runs on your server and people will have to build

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this on their own servers in order to test it.

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All of this is available on GitHub.

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The entire repository,

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all of the code,

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the entire application is available on GitHub.

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You can host this locally on your own machine with no problems whatsoever.

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It is written in Java and Python 3 and is very,

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very platform -independent,

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so you can ideally run it on any machine highly cost -effectively.

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Now we have a third terminal window here.

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This is to replicate our Ethereum network.

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This is called the Ganache

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This is responsible for actually running the Ethereum

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virtual network where we get duplicate credits.

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And this is responsible for creating blocks on the Ethereum blockchain.

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Now how the tamper detection works is every time a document is uploaded

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onto this dashboard,

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a hash gets created through the post API endpoint from

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our Spring Boot server.

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And then this hash gets recorded onto this Ethereum.

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chain the ethereum virtual chain a block gets created every time a hash is recorded and

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this hash is immutable which means it cannot be changed every time a document is

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created locally and a tamper happens locally that is if somebody

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were to have database level access and if they were to tamper with the

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document the hash of the document changes locally now if the hash changes locally

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it is compared with the anchored hash on the blockchain which like i mentioned will never change now

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if the anchored hash is different from whatever

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local hash is recorded.

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That difference in hash itself will explain to the dashboard that

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the document has been tampered with and the Django endpoint,

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the Django reads from the Spring API,

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REST API endpoint and then displays it accordingly on the dashboard.

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so now that we have the django dashboard running we have the database connected we have the ganache network

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listing on a port we also have a spring boot application running we can refresh this page and

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our dashboard should be working we have a basic accessibility accessibility feature here a

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light mode dark mode selector we can log in as an admin

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Just to show your role -based access.

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So if I were to enter admin credentials,

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I will be logged in as an admin.

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So if you can see you have multiple features here.

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You have case registry,

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which allows you to see all the cases that have been uploaded.

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We also have a list of documents to see all the documents that have been uploaded in

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the past.

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We have a home section where you

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can access every little tab and every tile independently from the home.

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We have an audit log,

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which is actually the part

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of this tamper detection feature that we've explained and this tamper detection feature

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works on the audit log it uses data from the audit log so you can see we have pre

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-upload tamper detected this are these are just some operations that happened in the past and

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if i were to go go to documents you can see we have multiple documents already mentioned here

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and all our actions are available here now the verify action is what helps you detect a

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tamper so i'm going to upload a document um let's just call

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it

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New test 2 .txt.

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So this is a document that I'm going to upload.

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I'm going to call this test tamper test case 001.

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Let's just call this a chart sheet.

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Just for an example,

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you can upload actual legal documents can be uploaded,

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but just to see how our dashboard behaves.

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It's going to upload a sample document here.

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Click on upload.

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If you can see the upload is successful.

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And now let's go to the document here.

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If you can see, this is the document I just uploaded.

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And now I'm going to click verify on this document.

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If you can see,

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it says, file matches the hash record in on -chain at upload time,

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which means this is successfully an untampered document,

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and it has been properly uploaded on the dashboard.

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Now, if I were to tamper with this document,

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let's say I'm somebody with database -level access,

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and let's say I go to our

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repository here,

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and inside Evidentia,

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I open the Spring Boot service,

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and let's say I have access to uploaded documents,

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which is a folder within the backend.

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So if I have database -level access,

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and if I were to tamper with this document,

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i'm just going to do a quick ls command to show you all the documents here and this is the new document that we uploaded

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so i am going to tamper with this document i'm going to do echo just an echo command

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to append a string to the document this is ideally how a tamper works if somebody were to

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edit the document illegally with the database level access or tamper is recorded

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so i'm going to tamper with this document and

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it should just be 258

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so this is our document and i'm going to tamper with this now

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the document has been tampered with let's go back to our dashboard

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and if you were to click on verify

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If you can see,

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file did not match the on -chain record,

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which means, like I mentioned,

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the hash is different when tampered with,

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and a different hash got recorded,

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which is compared with the anchored hash.

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And because it is different,

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we know that the tamper has been detected.

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The second part of the statement,

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restored to its last good known version,

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is ideally just a feature that is an example of tamper repair.

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Now, if a tamper happens,

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we just don't leave it like that,

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right? We actually have to fix the tamper.

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So it uses the audit log and a document control or a document version control.

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to store a separate copy of that original document in a remote database.

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Now,

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if a tamper were to happen,

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then it just takes the untampered version and then restores the original document

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with the untampered version.

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So it takes the tampered version and then replaces it with the original untampered version that was

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stored at the time of upload.

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This is version control and this we've used to actually fix tampers.

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So tamper repair is also happening and tamper detection is also happening.

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Now,

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if you were to click on verify again,

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You can see it says file matches the hash recorded on -chain because we fixed the document

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with the original document and the document has been repaired.

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So now that the hash matches,

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there is actually no error and we know that the document is now untampered

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and is back to its original state.

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Now this is post -tamper detection,

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but what if somebody were to tamper with the document even before they upload it to the dashboard?

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So this is something that we've addressed,

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which was a research gap that was found in multiple research papers pertaining to blockchain

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and chain of custody.

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Now how this works is I have a watcher

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script, if you can see.

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There is a watcher script here called watch .py.

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This script is responsible for watching the entire operating system.

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Now if somebody were to create a file at the very

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time of creation,

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even before uploading it to the dashboard,

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the hash of that file gets recorded.

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So if you can see it says watching this particular repository,

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which means it is constantly now watching that repository.

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And if I were to go to that repository here,

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Now we are inside this particular repository and I'm going to create a quick little test file just

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to show you new recording

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and This is a text

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file best file that I've created if you can see there is a hash that just got recorded here So

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it says a hash has been recorded for this particular document And if you can see the hash is

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now the same that is because we haven't tampered with the document But let's say I tampered with this

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document mind you this is happening all before we've actually uploaded it to

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the dashboard So I'm going to tamper with this document and

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Now, if you can see the hash has actually changed.

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So after I did a tamper string to the document,

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the hash has changed.

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So even before I have uploaded it to the dashboard,

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the watcher script knows that the hash has been changed.

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It sends the different hash to the rest API endpoint and the hash gets

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compared.

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So now if we were to upload the document to the dashboard,

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I am uploading this after I've actually tampered with the document.

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So I'm going to call this new recording.

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So this is our document here and

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I'm going to upload this.

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Now, if you can see,

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it says uploaded,

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but with a concern,

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the file's content differs from the version first locally observed at this time.

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It may have been modified after creation,

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but before upload,

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which means even before we've uploaded this,

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it knows that the document has been tampered with because the hash strings

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have changed.

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And this is all because of our watcher script,

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which entirely watches the entire operating system here.

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And every time a file gets created at the time of creation,

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it can detect a tamper and then report the tamper to our dashboard.

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So that is how pre -tamper detection works.

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This is a research gap that we've addressed successfully in our prototype.

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Now if we were to go back to documents,

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you can see we have an additional feature here called Evidentia Mind.

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Now Evidentia Mind is basically a document analysis feature which runs an LLM

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under the hood.

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You can use a local hugging face model or you can also use your

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own model through an API endpoint.

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Anything that works for you.

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If you're worried about security,

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run it locally.

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Ideally what this does is it analyzes the entire document,

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gives you a full summary of what the document is about.

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It gives you key dates of all the parties involved.

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So this is a sample of fire that I had uploaded previously and evidentia is clearly read

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through all the key dates.

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So the dates that crimes may have happened,

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the dates where a certain event would have happened,

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the exact time all of it is recorded,

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the key parties.

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So if let's say you're reading through a document and you may have missed something with the naked eye,

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but evidentia of mine can actually read through the entire document and give

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you clear details of everything.

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Those are the names involved,

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the key parties involved,

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what they may have done,

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the context from the document.

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It has a verification against the actual source as well.

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So the LLM is actually never wrong about what has happened and it's never going to hallucinate

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because it uses your actual source and has a near 99 to 100 % accuracy

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of whatever you've uploaded.

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So it reaches through your document and then reads through all of it,

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gives you details related to fine print.

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So let's say you have a legal fine print that you may have missed with the naked eye.

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So there is an asterisk.

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and a small string of text in the document that you may have missed,

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fine print, then this can actually read fine print for you and then give you the

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meaning of what it is,

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why it matters,

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why it is related to this particular FIR that you have uploaded,

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etc.

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So all of this is available through EvidentiaMind.

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It is an excellent feature to top it off alongside all the security features,

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pre -upload and post -upload tamper detection that we have.

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This is a feature that anybody can use using their own API key or their own local model

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if they're worried about security.

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And these are all the features that Evidentia offers.

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It is a full -fledged document management system with role -based access.

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You can log in as an officer as well.

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Officer 1.

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And if I were to log in

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as an officer.

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it is going to restrict content,

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and it is not going to show me all of the content that is available,

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if you can see.

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So this is how role -based access works.

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Officer has different roles.

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Admin is a global role,

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and anybody, as an admin,

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you can access all of the documents,

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the audit log,

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the actual documents that have been uploaded,

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et cetera.

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This is role -based access,

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and this is evidentia.

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This is a full demonstration of how the product works,

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and it is currently available on GitHub.

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You can run this on any machine,

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so it is completely platform -independent.

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Thank you.

EvidentiaSIHFinalDemo

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