The data defines software's ability of information processing. Better data means an increasingly more accurate and robust AI, which is able to solve real-time problems fast and error-free. Data labeling is one of the key processes that ensure data's quality and usability. Though, despite the essentiality of data labeling, many professionals often see it as a bothersome, routine task and prefer to focus on design and architecture features of AI. Following this, outsourced data labeling companies have become a new trend in AI and machine learning.

With how important data is for AI projects, today it is not enough to outsource labeling to the cheapest provider. Adaptability of the company to the new industry standards, their expertise and, surely, the security of their solutions are among the most important points you should look into when choosing a provider for your business, startup or project. While the first two are fairly clear, this article is going to focus on what are the possible risks when outsourcing data labeling and what it means to have a secure data labeling provider.

Types of Security Risks

Labeling involves protected or private data. Personally identifiable information, trade and classified information are all sensitive, meaning the disclosure of such data can pose a legal or reputational risk to subjects, projects or even whole companies or organizations. Most of the examples of security risks can be derived from either the environment staff members work in, their tools or themselves. For instance, an open unsecured workplace may potentially allow third parties to get a glimpse at confidential information; unsafe connection to the internet could expose the data during its transfer; improperly trained workers may lack the understanding of security protocols. Accounting for all of these, the vendor that handles your data must be equipped at every step of the process to guarantee you the security of:

Their Teams
Staff members who label data must undergo a background check, sign confidentiality agreement and be properly trained for the work at hand. Additionally, it is important for them to understand the context and the type of data they are dealing with.
Their Software and Hardware
Apart from a standard anti-malware software, the company's tech should be equipped with vulnerability protection, such as vulnerability scanner systems, and have excellent network security. It is a good practice to keep hardware security on the spot too — firewalls, routers, digital keys and switches can help with that.
Their Facilities and Workplace
The physical workplace must have an access control and labelers should be dealing with the data in a secure building where unauthorized personnel cannot see or retrieve it, intentionally or incidentally.

Tips for Protecting Your Data when Outsourcing

Finding the data labeling vendor that meets your security requirements can sometimes be tricky. Below you can find a few tips for security assurance and leak-proof outsourcing.

Look for a match
Simply said, when looking at the vast market of data labeling services, it is important to make sure that the company you are choosing is in sync with your practice or provides services that are specific to your project.
Value over cost
It might be tempting to go for the cheapest remotely located crowd-workers, however, many companies understand the obvious downsides of such an approach for both business and research, relating to quality, error rate and potential sensitivity of their data. Trust and reliance are among the benefits of choosing a specializing company as a solution.
Ask questions
Since we have already discussed the typical risk factors and fields in which security dangers may arise, you could leverage on that when choosing your data labeling company. Asking them questions is crucial in informing the best decision for your business, startup or project. A few universal questions you can use are:
  • What security certificates does the vendor hold?
  • What workplace protections and security policies are in place?
  • In which way is the security enforced?
  • Who has access to the workspace where the data is being labeled?
  • What kinds of training do data labelers receive?
Later on, established communication practices can also improve quality assurance.
Multiple levels of data classification
Implementing a few levels of data classification, for instance, "Public", "Sensitive" and ,"Confidential", enhances the security of your data over time and eliminates the risk of improper access within the company. Depending on the level of access available to a staff member, they will only be able to use the information they directly work with.
Sign non-disclosure agreement
In addition to the specifications in the contract, the industry standard procedure is for both sides to also sign an NDA (Non-disclosure agreement), securing the legal frames of what is considered confidential.
Veronika Gladchuk by Veronika Gladchuk
on April 14, 2020.