Sharing on AI Chatbot Platforms

A subject intentionally submits sensitive information when interacting with a public Artificial Intelligence (AI) chatbot (such as ChatGPT and xAI Grok). They will access the conversation at a later date to retrieve information on a different system.

A subject recklessly interacts with a public Artificial Intelligence (AI) chatbot (such as ChatGPT and xAI Grok), leading to the inadvertent sharing of sensitive information. The submission of sensitive information to public AI platforms risks exposure due to potential inadequate data handling or security practices. Although some platforms are designed not to retain specific personal data, the reckless disclosure could expose the information to unauthorized access and potential misuse, violating data privacy regulations and leading to a loss of competitive advantage through the exposure of proprietary information.

A Data Loss Prevention (DLP) solution refers to policies, technologies, and controls that prevent the accidental and/or deliberate loss, misuse, or theft of data by members of an organization. Typically, DLP technology would take the form of a software agent installed on organization endpoints (such as laptops and servers).

Typical DLP technology will alert on the potential loss of data, or activity which might indicate the potential for data loss. A DLP technology may also provide automated responses to prevent data loss on a device.

Domain Name System (DNS) filtering allows the blocking of domain resolution for specific domains or automatically categorized classes of domains (depending on the functionality of the software or appliance being used). DNS filtering prevents users from accessing blocked domains, regardless of the IP address the domains resolve to.

Examples of automatically categorized classes of domains are ‘gambling’ or ‘social networking’ domains. Automatic categorizations of domains are typically conducted by the software or appliance being used, whereas specific domains can be blocked manually. Most DNS filtering software or appliances will provide the ability to use Regular Expressions (RegEx) to (for example) also filter all subdomains on a specified domain.

DNS filtering can be applied on an individual host, such as with the hosts file, or for multiple hosts via a DNS server or firewall.

A Data Classification Policy establishes a standard for handling data by setting out criteria for how data should be classified and subsequently managed and secured. A classification can be applied to data in such a way that the classification is recorded in the body of the data (such as a footer in a text document) and/or within the metadata of a file.

An Acceptable Use Policy (AUP) is a set of rules outlining acceptable and unacceptable uses of an organization's computer systems and network resources. It acts as a deterrent to prevent employees from conducting illegitimate activities by clearly defining expectations, reinforcing legal and ethical standards, establishing accountability, specifying consequences for violations, and promoting education and awareness about security risks.

A web proxy can allow for specific web resources to be blocked, preventing clients from successfully connecting to them.

An agent capable of Endpoint Detection and Response (EDR) is a software agent installed on organization endpoints (such as laptops and servers) that (at a minimum) records the Operating System, application, and network activity on an endpoint.

Typically EDR operates in an agent/server model, where agents automatically send logs to a server, where the server correlates those logs based on a rule set. This rule set is then used to surface potential security-related events, that can then be analyzed.

An EDR agent typically also has some form of remote shell capability, where a user of the EDR platform can gain a remote shell session on a target endpoint, for incident response purposes. An EDR agent will typically have the ability to remotely isolate an endpoint, where all network activity is blocked on the target endpoint (other than the network activity required for the EDR platform to operate).

An agent capable of User Activity Monitoring (UAM) is a software agent installed on organization endpoints (such as laptops); typically, User Activity Monitoring agents are only deployed on endpoints where a human user Is expected to conduct the activity.

The User Activity Monitoring agent will typically record Operating System, application, and network activity occurring on an endpoint, with a focus on activity that is or can be conducted by a human user. The purpose of this monitoring is to identify undesirable and/or malicious activity being conducted by a human user (in this context, an Insider Threat).

Typical User Activity Monitoring platforms operate in an agent/server model where activity logs are sent to a server for automatic correlation against a rule set. This rule set is used to surface activity that may represent Insider Threat related activity such as capturing screenshots, copying data, compressing files or installing risky software.

Other platforms providing related functionality are frequently referred to as User Behaviour Analytics (UBA) platforms.

An agent capable of User Behaviour Analytics (UBA) is a software agent installed on organizational endpoints (such as laptops). Typically, User Activity Monitoring agents are only deployed on endpoints where a human user is expected to conduct the activity.

The User Behaviour Analytics agent will typically record Operating System, application, and network activity occurring on an endpoint, focusing on activity that is or can be conducted by a human user. Typically, User Behaviour Analytics platforms operate in an agent/server model where activity logs are sent to a server for automatic analysis. In the case of User Behaviour Analytics, this analysis will typically be conducted against a baseline that has previously been established.

A User Behaviour Analytic platform will typically conduct a period of ‘baselining’ when the platform is first installed. This baselining period establishes the normal behavior parameters for an organization’s users, which are used to train a Machine Learning (ML) model. This ML model can then be later used to automatically identify activity that is predicted to be an anomaly, which is hoped to surface user behavior that is undesirable, risky, or malicious.

Other platforms providing related functionality are frequently referred to as User Activity Monitoring (UAM) platforms.

Google's Chrome browser stores the history of accessed websites and files downloaded.

On Windows, this information is stored in the following location: C:\Users\[Username]\AppData\Local\Google\Chrome\User Data\Default\Login Data. This file is a JSON file and can be opened in any text editor, such as Notepad. This contains the URL, page title, date added, and date the bookmark was last used.

Google's Chrome browser stores cookies that can reveal valuable insights into user behavior, including login details, session durations, and frequently visited sites.

On Windows, this information is stored in the following location:

C:\Users\[Username]\AppData\Local\Google\Chrome\User Data\Default\Network\cookies.

This database file can be opened in software such as DB Browser For SQLite. The ‘cookies' table is of interest to understand recent activity within Chrome.

Google's Chrome browser stores the history of accessed websites and files downloaded.

On Windows, this information is stored in the following location:

C:/Users/<Username>/AppData/Local/Google/Chrome/User Data/Default/

On macOS:

/Users/<Username>/Library/Application Support/Google/Chrome/Default/

On Linux:

/home/<Username>/.config/google-chrome/Default/

Where /Default/ is referenced in the paths above, this is the default profile for Chrome, and can be replaced if a custom profile is used. In this location one database file is relevant, history.sqlite.
 

This database file can be opened in software such as DB Browser For SQLite. The ‘downloads’ and ‘urls’ tables are of immediate interest to understand recent activity within Chrome.

Google's Chrome browser stores some login data of accessed websites, that can provide the URLs and usernames used for authentication.

On Windows, this information is stored in the following location:

C:\Users\[Username]\AppData\Local\Google\Chrome\User Data\Default\Login Data.

This file is a database file and can be opened in software such as DB Browser For SQLite. The ‘logins’ and ‘stats’ tables are of immediate interest to understand saved login data.

The passwords are not visible as they are encrypted. However, the encryption key is stored locally and can be used to decrypt saved passwords. The key is stored in the file C:\Users\[Username]\AppData\Local\Google\Chrome\User Data\Local State, which can be read with any text editor, such as Notepad, and searching for the “encrypted_key” value. The tool decrypt_chrome_password.py (referenced) can decrypt the AES-encrypted passwords to plaintext.

Microsoft's Edge browser stores the history of accessed websites and files downloaded.

On Windows, this information is stored in the following location:

C:\Users\<Username>\AppData\Local\Microsoft\Edge\User Data\Default\

On macOS:

/Users/<Username>/Library/Application Support/Microsoft Edge/Default/

On Linux:

/home/<Username>/.config/microsoft-edge/Default/

Where /Default/ is referenced in the paths above, this is the default profile for Edge, and can be replaced if a custom profile is used. In this location one database file is relevant, history.sqlite.
 

This database file can be opened in software such as DB Browser For SQLite. The ‘downloads’ and ‘urls’ tables are of immediate interest to understand recent activity within Chrome.

Mozilla's Firefox browser stores the history of accessed websites.

On Windows, this information is stored in the following location:

C:\Users\<Username>\AppData\Roaming\Mozilla\Firefox\Profiles\<Profile Name>\

On macOS:

/Users/<Username>/Library/Application Support/Firefox/Profiles/<Profile Name>/

On Linux:

/home/<Username>/.mozilla/firefox/<Profile Name>/

In this location two database files are relevant, places.sqlite (browser history and bookmarks) and favicons.sqlite (favicons for visited websites and bookmarks).
 

These database files can be opened in software such as DB Browser For SQLite.

Depending on the solution used, web proxies can provide a wealth of information about web-based activity. This can include the IP address of the system making the web request, the URL requested, the response code, and timestamps.

An organization must perform SSL/TLS interception to receive the most complete information about these connections.