In this rerecorded video, we're going to talk about what is Osint, also known as Open Source Intelligence.
Now this is a rerecorded video.
If you're coming back to it, one of the students in one of the classes did mention that the audio quality
was a little too quiet, and I apologize for that.
I I'm not sure what happened.
So this is why I'm rerecording this and hopefully the audio quality is a lot better.
So let's get into what is Osint, also known as open source intelligence.
So open source intelligence or Osint, is data collected from publicly available sources to be used
in an intelligence context in the intelligence community.
The term open refers to overt, publicly available sources as opposed to covert or clandestine sources.
It is not related to open source software or public intelligence, and that's a Wikipedia entry for
Osint.
Now what can we find with Osint?
Well, we can find a lot of different things using open source intelligence, things like email addresses,
phone numbers, addresses and identities.
It can be used for background checks, social media accounts and information.
We could do criminal record searches.
We could figure out different scams and a lot of other things.
So in terms of who would use Osint, pretty much everyone can use Osint on a certain level.
Law enforcement will use Osint to search out possible crimes and information security professionals.
Malicious hackers use Osint in terms of malicious hacking.
Um, that's referred to as reconnaissance, which is the first phase of hacking.
Businesses could use Osint to check up on competitors, things like geopolitical changes, climate changes.
They could take a look at their employees.
Investigators will use open source intelligence for things like doing background checks, finding things
about people, finding missing people, and so forth.
Journalists will generally use Osint to do fact checking and dig up information on different stories,
and home users could use Osint for things like, well, determining if something is a scam, looking
up information, just reading the news nowadays, there's so much information out there, it's hard
to tell what's real and what isn't real.
And you could use Osint to kind of determine fact from fiction.
Now, informational data use using open source tools.
We're going to be there's a lot of different tools out there for using Osint.
Things like CSI Linux, Maltego, Google events operators, news sites, etc. and we're going to be
taking a look at all these different tools and how to use them, when to use them.
So by piecing together information we can, it helps us build a much bigger picture in terms of things
like Facebook usernames.
We can take a Facebook username from that.
We could generally figure out a different Facebook post.
User email addresses, data breach dumps.
We could take a look at password hashes, unique passwords, users real email addresses.
We can potentially figure out their real identity and location.
Basically, what we're doing is we're gathering information and we're analyzing that information on
our target, whether it's a person, whether it's a location, a story or whatnot.
And that data could lead.
If we're dealing with people to other users that we can that interact with our target.
And essentially by piecing together this information bit by bit, we're ultimately building a broader
picture of what we're looking for.
So in terms of employee or employers rather checking on their employees, they may check social media.
So in this case a KFC worker was fired after a Facebook photo shows her licking potatoes and the person
that snapped the photo was also fired in this case, so employers may use ozone to monitor social media
activity related to their company or employees.
So this can help employers figure out if their employees are potentially doing anything that it will
damage their reputation.
And in this case, they did find out something that was very damaging.
So when licking your food, potentially, or even if they're just pretending to and and they're posting
videos that can have a very, very bad impact on your company, to say the least.
So, uh, in terms of dating sites, men and women, if they're on a dating site, individual may use
Austin to verify individuals they're considering going on date with.
And if that information is accurately represented, representative of that person.
So say a woman goes on, uh, a dating site and they see a picture of someone and they, they look at
the photo and go, well, that's a good looking person.
And they like the bio and they start chatting and things sound really great.
Well, before they go on that date, they may use open source intelligence to start looking at this
person and going, okay, is this person real?
They could potentially take the photo, do a reverse image search, see if that's really them, start
background checking their stories and information and help determine if that person is scamming them
or God forbid, that they are a dangerous individual going on these dating sites and looking to hurt
someone, whether it's a man or a woman, law enforcement or network administrators may be after malicious
hackers, and you could potentially use OSN to track down malicious hackers, scammers and whatnot.
And potentially what networks are tied to using open source intelligence.
So a lot of times in my work, I come across people that are sent phishing emails or potentially scams.
Information to different users all use open source intelligence, one to determine whether it is a legitimate
email or text message.
And then I'll generally take that information and go through and figure out kind of who they are and
where they're at.
So these are some uses for open source intelligence and kind of a broader view of what open source intelligence
or Osint is.
Now there's a lot of specific aspects of Osint.
Things like Signet which is signal intelligence.
Marsden.
Um, there is um Humint which is human open source intelligence.
This is going to be a broad picture of open source intelligence.
We're going to take a look at different tools and techniques for your general use of open source intelligence.
And I'm really excited that you're taking this course.
I appreciate it very much.
And again, hopefully the audio quality is much, much better in this one.
And if you run any questions with the uh, or have any problems with the course, always feel free to
contact me.
Thank you so much.
I'll see you in the next video.