Data Privacy and Data-Centric Security at IAPP

Data-Centric Security
Data Privacy and Data-Centric Security at IAPP

The International Association of Privacy Professionals (IAPP) held its Global Privacy Summit in Washington DC March 4-6. The topic of Data-Centric Security was presented by Informatica’s Robert Shields, Product Marketing, Data Security Group.  Here is a quick recap of the conversation in case you missed it.

In an age of the massive data breach, there is agreement between security and privacy professionals that we must redefine privacy policies and controls. What we are doing is just not working effectively. Network, Host and Endpoint Security needs to be strengthened by Data-Centric Security approaches.  The focus needs to be on using data security controls such that they can be enforced no matter where sensitive or confidential data proliferates.

Data-Centric Security does not mean ‘encrypt it all’. That is completely impractical and introduces unnecessary cost and complexities. The approach can be simplified into four categorical steps: 1. Classify it, 2. Find it, 3. Assess its risk, 4. Protect it.

1. Classify it.

The idea behind Data-Centric Security is that based on policy, an enterprise defines its classifications of what is sensitive and confidential then apply controls to that set of data. For example, if the only classified and sensitive data that you store in your enterprise is employee data, than focus on just employee data. No need to boil the ocean in that case.  However, if you have several data domains of sensitive and confidential data, you need to know where it resides and assess its risk to help prioritize your moves.

2. Find it.

Discover where in your enterprise sensitive and classified data reside. This means looking at how data is proliferating from its source to multiple targets – and not just copies made for backup and disaster recovery purposes.

For example, if you have a data warehouse where sensitive and confidential data is being loaded through a transformation process, the data is still considered classified or sensitive, but its shape or form may have changed. You also need to know when data leaves the firewall it becomes available to view on a mobile device, or accessible by a remote team, such as offshore development and support teams.

3.Assess its risk.

Next, you need to be able to assess the data risk based the number of users who may have access to the data and where those users are physically located and based on existing security controls that may already exist. If large volumes of sensitive data is potentially being exposed to a large population in another country, you might want to consider this data more at risk than a few number of records that are encrypted residing in your protected data center. That helps you prioritize where to start implementing controls to maximize the return on your efforts.

4. Protect it.

Once you have a sense of prioritization, you can then apply the appropriate, cost effective controls that aligns with its level of risk.  Place monitoring tools around the sensitive data and detect when usage patterns become unusual. Train on normal user behavior and then initiate an alert to recommend a change to the application of a control.

In a world where policies are defined and enforced based on data privacy regulations and standards, it only makes sense to align the right intelligence and controls to ensure proper enforcement. In reality these four steps are complex and they do require cross-functional teams to come together and agree on a strategy.

Comments

  • Vanessa

    i can only hope to strike back quicker stronger and smarter, in by protecting our data privacy