Disclaimers. You should really really read this!
First off - some disclaimers. I know nobody ever reads disclaimers but these are pretty important so you really need to read them.
Disclaimer 2: People have the same names. Who would have thought?! You find someone in the data and go 'oooh! Het jou katvis!' - but remember that it could be someone else with that same name. Manually verify results - always!
Disclaimer 3: The data is not very clean. There could be four entries for the same person and in Maltego these nodes will not merge (different node_IDs). You'll need to manually merge them if you feel like it. Of course, see 2 - e.g. they could be four different people. The same goes for addresses - the data was clearly captured by hand, so people write the same address in many different ways. Best thing here is to take the most significant part of the address and search for that - then manually verify.
Disclaimer 4: The transforms might break. I am not even a proper coder. It should be OK, but when a query does not return or stuff falls apart then remember this disclaimer. If we get a LOT of interest on this then we might rewrite the transforms properly. Also - there's a lot of improvements that can be made on the transforms. Display info etc. etc. Don't tell us - we know this.
This was hacked together on a Friday afternoon and a Saturday night and by the end of the day it seemed very useful and that's why we're releasing it now.
With that out the way, let's first see how to get the transforms and entities into Maltego. We thought about adding this into the Transform Hub but decided against it. It's cool, but it's not THAT cool. That means you need to install the transforms by hand. Luckily, it's pretty easy.
How to installIn the transform hub, click on the [+] sign. Fill in the fields as you wish. The only part that needs to be the same as our example is the seed URL. The seed URL is [https://bark.paterva.com:8081/iTDSRunner/runner/showseed/PanamaPapers]
Once you filled it in hit OK. You'll now see the item appears in the transforms hub:
Hover over it and click on 'Install'. It should look something like this when you're done (this is Maltego 4, but the other versions should look similar):
Woot! Now you're ready to start using the transforms.
How to use
Before we start we want to quickly discuss the data. There are 4 tables. Officers (people), Entities (companies, trusts or other legal entities), Addresses (duh - addresses), Intermediaries (think agents or companies or people doing the work on behalf of the officers). Then there's a table that links all of these together.
There are 4 entities in Maltego - Officers, Entities, Intermediaries, Addresses and Country. The transforms implement an almost fully meshed grid between these with a couple of spaces where it's not really applicable.
The starting point for all transforms is a Phrase. As the data is mostly linked by node IDs you cannot start with any of the 'PanamaP' entities as you don't know what the node ID is. You always start with a Phrase and search from there.
Let's see how this works. Let's assume we're looking for an officer called 'Hillary Clinton'. We suggest looking for just the word 'Clinton'. We drag a Phrase entity (in the Personal section) onto the graph, double click on the text and change it to 'Clinton'. Then we right click on the entity to bring up the context menu, navigate all the way to the top (right click on the menu) and select the Panama Papers transforms:
In that group we select the 'PP Search officer' transform:
Let's assume we're interested in one of the nodes and want to see what entities and addresses are connected to that officer. We select one of the nodes, right click and run the 'PP Get details' transform:
We can do the same on the Entity that's returned from here:
And so the story goes on...
Another interesting way to look at the data is to start looking for the Addresses. This is sometimes useful to identify Officers from certain locations. For broader searches you can start from a country...
Let's see which officers stays in Beverly Hills. We start with a phrase 'Beverly Hills' and run the 'PP Search addresses':
We get 47 addresses in Beverly Hills that's in the database. Let's see what's going on there. We select all the nodes and run the transform 'PP To officers or entities here' transform:
Does 'Beverly Hills' exist in other countries too? Yes. In Australia. In Hong Kong. Probably in other countries too. So we need to remove them. Control F, type in 'Hong'. Hit find. Control shift down arrow (select children). Delete. Rinse and repeat for others. Hmmm.. perhaps Beverly Hills was a bad choice. There's even a Beverly Hills in Balito, South Africa. Really? REALLY?
Anyhow. Rinse. Repeat. And then:
Pretty please read the disclaimers at the start of this post. You probably scrolled to the end right away. But please read them.
And this time, for realsies -- use responsibly!