Detecting Software Supply Chain Anomalies by ‘Shifting Left’ With Analytics

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Detecting Software Supply Chain Anomalies by ‘Shifting Left’ With Analytics

Even though many enterprises stand to benefit immensely from the new landscape where software is the product, the side effects of security flaws in software are having a negative impact on business results in the form of lost brand reputation, loss of customer trust, and financial fines.

Organizations are battling to stay ahead of compliance difficulties and software vulnerabilities in order to keep their products and services viable, especially with at least 17,447 new vulnerabilities discovered in 2020 alone. The race is fueled by the pressure to provide products and updates faster than ever before, complex multi-cloud architectures, a scarcity of application security experts, and complicated deployment environments that make it difficult to see everything that could go wrong at any given time.

Should enterprises ‘shift left’?

Organizations should take a security-focused approach to get ahead of this vicious growth cycle. This mindset is referred to as “shift left” or “shift left testing.” It entails testing software earlier in the process and re-testing it throughout the development cycle. With tighter timelines and increasing demands on everyone involved in the development process — from DevOps to security teams to developers — this work can be intimidating and difficult to scale.

Also Read: Why Security Takes the Top Spot in a Digital-First Market?

Using automation and analytic tools is one method to scale the ability to test more code in less time. Enterprises can detect vulnerabilities, security issues, and places where malicious behavior can begin at their source, at scale, before they move into production supply chains by integrating security into the DevOps process during the code, build, and run stages — rather than as a separate set of tasks performed by the security team.

Incorporating security analytics into the development and build/deploy processes not only aids in the detection of potential security breaches, but also allows for the early identification of artifacts that can be remedied, avoiding the finger-pointing that is common in post-mortem security investigations.

Software supply chain attacks

In terms of techniques, recent attacks have turned away from brute-forcing software vulnerabilities and toward embedding backdoors into software as part of the development supply chain. These are designed to allow threat actors to gain access to and poison software updates, which they can then use to compromise any company that uses them.

A lack of security monitoring and control for software coding and delivery can jeopardize the supply chain, creating a severe security gap. Because security testing does not check for changes in software systems, the gap widens. With the increasing velocity of software changes in the cloud computing era, this security gap is becoming even more problematic.

Also Read: Embracing and Securing Infrastructure as Code

Security monitoring of CI/CD Pipelines

Code modifications and security monitoring of CI/CD pipelines can aid in the detection of supply chain attacks and provide an additional layer of security to a business-critical cycle. Monitoring for hostile behavior on the network usually entails detecting activities that are the result of compromised accounts, which can be difficult to uncover without contextual data. Companies should monitor access to network resources and keep logs that can help security teams investigate anomalies, according to the National Institute for Standards and Technology.

Security monitoring is becoming more difficult in the cloud, where code now supports everything that was once constructed from hardware. Infrastructure as code (IaC) is critical in the modern cloud CI/CD environment because most enterprises rely on cloud assets. One factor that increases the need for proper security monitoring is the criticality of code that builds cloud infrastructure. This can help detect malicious activity by users who appear to be legitimate.

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