Digital transformation has driven the rapid adoption of cloud-delivered services like SaaS/IaaS/PaaS in enterprises. This, in turn, has resulted in the migration of digital assets (aka data) from the confines of enterprise data centers to the cloud data centers that are not under the control of the enterprises. Additionally, the onset of the COVID-19 pandemic has resulted in remote work becoming the norm. These trends have, in turn, forced a security transformation from the traditional stack of security appliances deployed in an enterprise data center to cloud-delivered security. Gartner has coined the term security service edge (SSE) to represent this new platform where security services like secure web gateway, cloud access security broker, zero trust network access, egress firewall, etc. are delivered in the cloud to safely enable users to perform their work and to reduce the risk of getting compromised and losing data.
There are a few key capabilities that are critical to SSE solutions:
- Zero Trust Data Access – SSE solutions enforce security policies for accessing data based on contextual information like user, device, application, application risk, user activity, user risk, etc. This contextual information becomes the virtual badge that allows/denies/coaches a user’s access to an enterprise’s digital assets
- Insider Threat Detection – Enterprise users (employees, contractors) are entrusted with access to business-sensitive data to carry out their work. Security controls are needed to ensure these insiders do not inadvertently or maliciously exfiltrate the sensitive data thereby putting the business at risk.
- External Threat Detection – Every enterprise is under attack from external bad actors looking to compromise the coveted data for monetary or strategic control purposes. These actors can be individual hackers and organized cybercrime groups, as well as nation-states. The attacks can be phishing, malware, ransomware, or even sophisticated APT attacks. SSE solutions provide effective threat detection, prevention, and remediation services as an added layer of defense to enterprises to protect their data.
The role of AI/ML in SSE solutions
The key underpinning of a powerful SSE solution is the ability to extract very rich contextual information when processing network traffic and enforce the zero trust data access policies. Some of the inputs needed for making the data access decision are the sensitivity of the data leaving an enterprise as well as indicators of threat in data coming from external sources. These are areas where artificial intelligence (AI) and machine learning (ML) have proven to be invaluable in enhancing the fidelity of detections. Let’s look at this in more detail:
Sensitive data classification
Legacy data security solutions use a combination of regular expressions, keywords, and dictionaries to identify sensitive data. This is very error-prone and leads to excessive false positives and in turn, adds a burden to security analysts to sift through mounds of alerts to identify the real violations.
Machine learning-driven data classification can significantly reduce this burden and provide high fidelity classification verdicts. Natural language processing (NLP) algorithms are very conducive to solving this problem. NLP models have been developed by Netskope to classify