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Is Generative AI Soon to Become a DevOps Cybersecurity Threat?

Extended capabilities come with additional tools, but new weaknesses are also added. Before allowing team members to make extensive use of new tools, business and IT leaders must fully comprehend their effects.

More than half of senior IT professionals are giving generative AI top priority for their companies in the next months, according to a March 2023 Salesforce study. However, a staggering 71% of those users think that these tools will “introduce new security risks to data.”

You can find it everywhere you look. Artificial intelligence is streamlining everything, from photo editing to essay writing. It can be applied to streamline developer workflows and automate procedures. But because these capabilities are so new, it’s unclear how they will affect cybersecurity.

The easiest way to ensure success when these technologies are completely integrated into your IT system is to be aware of the potential weaknesses.

Three key areas of getting ready for these possibly game-changing tools will be examined:

1. AI Implementation Challenges

2. Data Security Issues With AI

3. Using DevSecOps tools to address these issues

AI Implementation

Organizations are rushing to use generative AI tools due to their popularity, which may result in missed mistakes. By taking your time during the early setup phase, you can avoid making costly mistakes after your staff is incorporated. AI solutions are excellent for streamlining operations.

Once they are set up, these technologies can significantly speed up how quickly your team produces projects. This velocity also makes the tools more difficult to regulate, even though it could seem ideal for business processes. Anything less than complete transparency into the state of the code results in technical debt, problematic applications, and security issues.

Generative AI functions by predicting the most appropriate response to a query based on data that has already been entered. These tools are much better than anything we’ve ever had, yet they’re still far from ideal. Not only is bad coding feasible, it’s also likely.

Utilizing generative AI tools when developing a DevOps strategy necessitates deliberate actions from the beginning. Don’t let your desire lead you to take unnecessary precautions and dive straight in.

Data Security Issues With AI

A generative AI tool’s queries don’t always provide the same outcomes. This is intentional in what is referred to as a “probabilistic model.” Although this idea might be excellent for building conversational chatbots, it can be challenging to implement in code.

Direct familiarity with the subject is necessary for reliable coding. However, the foundation of these tools is a feedback loop that incorporates input data from both knowledgeable and uninformed sources.

It’s possible for the coding structure to include defects and logical mistakes. If coding flaws are not detected in a timely manner, they may result in data security issues due to hallucinations and false assumptions.

Sensitive data may be accidentally deleted or exposed as a result of faulty apps and a large amount of technical debt. No business can afford to take this kind of risk, but businesses in regulated sectors have a greater incentive to protect sensitive data because doing so will keep them in compliance with data security laws.

Total control is required to fulfill these needs, and generative AI techniques can only help with this when used intentionally.

Using DevSecOps Tools to Solve These Problems

The speed at which you can create a sentence is a direct result of the fact that you are reading this article. Using a number of DevSecOps tools as safety nets is one method to release these automatic code modifications safely.

Automated scans of this code are carried out in a variety of methods using static code analysis. In the beginning, it catches faults as they are written, allowing developers to go straight to the generated code and remedy anything noted. Second, it can search your current environment for inherited flaws that might endanger your system.

Through automated integration and deployment technologies, several testing gates can be established. Even if code changes originate from several sources, these additional tests on the update’s structure make sure everything functions as intended.

A safety net is a must for every security strategy. Along with AI tools, regular data backups and the capacity to quickly restore this data should be provided. Regarding how these technologies will alter your system and how it functions, there are just too many unanswered questions. A recent backup guarantees that you will have a backup in case of a major outage.

Getting Ready for the Inevitable

We will engage with our IT infrastructure differently thanks to generative AI tools. To safely deploy these tools, maximize their benefits, and reduce data security risks as much as possible, preparations must be done right now.

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