Identifying and Avoiding Insider Threats with AI
Most companies consider outer threats as the biggest but little do they know, insider threats can create more detrimental conditions. None can be riskier than a hacker or a forger who functions at the heart of your business and have access to even the littlest details. Moreover, insiders have the power to leak inside data and put it at risk, intentionally or by mistake; something that outsiders can hardly do.
Many reliable reports have suggested that insider threats have increased at a radical scale while causing serious concerns to companies of any domain. As per the 2016 Osterman Research Survey, stolen database credentials and recognizing critical gaps in database security were ranked as the foremost database security threats.
Thankfully, the stats have steered the attention of companies, coaxing them to invest smartly in securing databases and database infrastructure. Companies are going with newer ways that are backed with powerful technologies like AI and ML to secure their crucial data from attackers.
Augmenting Data Loss Prevention (DLP) with AI
In 2020, over 3,950 confirmed cases of data breaches were reported by Varionis, a cybersecurity company. To not become another data breach example, companies are emphasizing more on data loss prevention (DLP) where they can identify exploited business data, monitor data usage, and enforce security policies to avert users from accessing and misusing sensitive data. To add more potential to DLP, enterprises are integrating AI predictive technologies to control data breaches within the company. AI-backed DLP ensures greater accuracy to recognize unintentional and alleged insider breaches, forgeries, and other threats.
Introducing the Data Flow Model
It indeed is the biggest challenge to differentiate between a loyal insider and an attacker in today’s challenging networks. This is where various promising AI forms like deep protocol analysis, ML, and behavioural analysis come into use.
Artificial Intelligence, Machine Learning, and behavioural analysis possess the ability to recognize insider threats by incorporating a model of basic actions of the data flows in the database infrastructure. Here data flow represents all the attributes required to comprehend and develop database with patterns. For better understanding, here is an example, a database user will have to specify the server, table name, database, schema, in read or write mode to access a table. Not only this, s/he will equip specific contexts like database management system port number, username, client IP address, and service name.
Artificial Intelligence, Machine Learning, and Behavioral Analysis
AI-powered security appliances hold the potency to find all databases without any interference, even all those databases that might get missed during the manual audits. It has helped many enterprises in securing their stolen database credentials along with insider threats. These applications automatically develop a fundamental prototype of legitimate activity by recognizing all the data sets while analyzing their data flows. It compares new data patterns against the existing alerting companies to take required actions, in case an attacker is stealing credentials. This method has given proven results for being accurate with giving the minimal chance of false-positive alerts.
