Three Ways AI Is Reshaping DevSecOps
Developers can evaluate their organization's security posture with these three AI-driven DevSecOps trends.
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With the integration of AI-enhancing developer tools, DevSecOps workflows are becoming faster and more efficient. From influencing how organizations operate on a daily basis to closing the developer skills gap, reducing testing and remediation time, and decreasing tool sprawl, AI’s benefits continue to increase tenfold with every advancement. However, cybercriminals are also using AI to create advanced malware, putting security at the top list of developer priorities. Developers are accustomed to incorporating security within software applications, but organizational roadblocks slow down production. As organizations shift from DevOps to DevSecOps, the modern developer’s role is becoming more closely tied to security measures. Let’s explore three ways AI is reshaping DevSecOps and how developers can help evaluate their organization’s security posture.
1. AI Is Evolving the Software Development Life Cycle
Security threats are becoming increasingly sophisticated. One of the primary challenges nearly 46% of developers face is a lack of security expertise. Pressed for tighter product and application deliveries in the software development life cycle (SDLC), developers are often forced to look past incorporating security measures upfront, which eventually slows down production and time to market.
According to a recent Gartner report, by 2027, DevSecOps practices will be embedded in 85% of product development teams, as opposed to only 30% in 2022. But that jump in implementation will require a cross-company cultural mindset shift led by leadership. By incorporating proactive security operations and checks from the beginning of the SDLC, developers can focus on other strategic functions, leaving AI to manage testing, coding, monitoring, and administrative tasks. As a result, faster remediation and updates enable developers to hit their delivery timelines while incorporating security within the SDLC.
2. AI Is Closing the Skills Gap
While economic uncertainty has certainly impacted the majority of tech companies, smaller development and security teams are bearing the brunt of layoffs and budget cuts. Limited resources and less talent is a risk to any organization’s security posture, especially if vulnerability mitigation is deprioritized and left unchecked.
By leveraging AI to run vital security tests, developers are more productive and can use the saved time to reskill. Utilizing AI to scale developer productivity and workflows levels the playing field for smaller startups competing with larger enterprises. Highly skilled developers and cybersecurity professionals are still sought after, but with AI-powered tools and automated processes, teams can rely on technology to close the talent gap.
3. AI Is Decreasing Tool Sprawl
Managing tech stacks and reducing tool sprawl is a benefit of utilizing AI within developer workflows. With more data-driven AI tools in the market, developers can focus on using a suite of products from one vendor rather than cobbling together best of breed options, which leads to tool sprawl. Security teams often don’t know how many tools - or even which tools - developers are using. This poses a significant risk to an organization’s security posture and budget. We know this to be a huge benefit to customers who use software developer kits (SDKs) like LEADTOOLS. With a wide breadth of technology available to developers, it serves as a one-stop-shop, allowing the dev team to centralize around one toolkit vendor. While AI identifies vulnerabilities and bugs before code fully gets integrated into a program, SDKs save developers time by having security measures already in place, keeping folks on every side of the SDLC happy.
By increasing cross-collaboration and focusing on using a suite of tools with the most ROI, developers can integrate security testing more easily within their existing toolset and help manage data governance. Organizations can also issue a company-wide protocol about IT-approved tools, which can help security teams have better visibility into third-party tools.
How To Evaluate DevSecOps Maturity
Now that we’ve gone over how AI is reshaping DevSecOps, below is a brief guideline for how developers can evaluate where their organization sits within the DevSecOps roadmap and how to improve overall maturity.
- Identify areas where AI could streamline processes and where there are security concerns. By focusing on improvement areas, security measures can be set up as needed.
- Train and upskill talent for security protocols, AI, and cross-collaboration best practices. With a tight talent market, the best way to leverage talent is to invest in professional development or easy-to-access SDKs that address the skills gap directly.
- Assess the existing tech stack for effective and efficient developer tools and how often they are used. A lack of tool usage can lead to wasted budget and a bloated tech stack when there are many toolkit options on the market that may better suit developer’s needs.
- Outline areas for how DevSecOps implementation impacts AI tools and creates more streamlined IT infrastructure changes. This step won’t happen overnight, but a slow rollout can make a difference in consistent adoption.
AI is reshaping developer workflows via consistent testing within the SDLC, supporting the current developer and security talent pool, and streamlining tech stacks. Mass AI adoption will push forward the developer landscape beyond its current scope, and with it will come the evolution of developer roles. One thing is certain: AI is accelerating security efficiency, which can only benefit developers and application quality if handled correctly.
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