How IaC Turns Infrastructure Into a Competitive Advantage—Q&A With Srilakshmi Bharadwaj

As distributed systems scale, manual infrastructure processes frequently become a leading source of operational risk. Issues such as configuration drift, ticket-driven provisioning, and inconsistent environments can lead to higher costs and diminished performance. Infrastructure as Code (IaC) enables companies to improve efficiency. When implemented properly, it removes inconsistency from infrastructure and makes its processes reproducible and auditable.

By creating IaC, teams reduce drift and make deployment more predictable. They also recover faster because the desired state is already defined. The question for leaders is whether their automation strategy effectively supports long-term scalability and compliance readiness. Srilakshmi Bharadwaj has extensive experience in architecting and operating large-scale cloud and infrastructure environments, with a focus on distributed systems, platform engineering, and governance-driven infrastructure services. In this Q&A, Bharadwaj discusses the optimal use of IaC and what fails in real enterprise environments when governance and automation are misaligned.

Q: What are the hidden risks in manual infrastructure management, and why is IaC an effective mitigator of those risks?

Bharadwaj: The biggest hidden risk in manual infrastructure is inconsistency. Manual infrastructure typically looks manageable until it isn’t. Systems start in a stable state, but engineers can configure them in slightly different ways. Over time, that subtle drift can lead to issues ranging from inefficient production to slow incident recovery to noncompliance. Drift also damages auditability, leaving no clear record of what changed, when, or why.

IaC ensures infrastructure is created and managed from code. The key to success is not to view IaC as a scripting technique but rather to see it as a full-fledged systems-level operating model, a platform with standards and measurable outcomes in place to ensure long-term consistency and success. LinkedIn is an example of how IaC benefits an organization. Looking to sustain platform growth more easily, LinkedIn used IaC to introduce uniformity across its engineering teams. Since that move, its platform has grown to serve more than one billion registered members worldwide.

Q: What is the best way to transition to IaC and minimize disruptions to operations?

Bharadwaj: Phased adoption is the safest approach to introducing IaC within an organization. With a phased adoption approach, companies begin in a low-risk, repeatable environment, like dev or sandbox. They then use standardized modules and add policy checks. After that, they move toward production-critical stacks. Additional steps include keeping manual fallback paths, automating one domain at a time, and using pipelines that validate security and compliance before rollout. The National Institute of Standards and Technology’s (NIST’s) DevSecOps guidance, particularly through the Secure Software Development Framework (SSDF) (SP 800-218), strongly emphasizes integrating security and verification into the delivery flow rather than treating them as an afterthought.

Other considerations for companies include actively driving cultural and organizational shifts to align with the new system. When utilizing IaC, the shift is about more than technology. Teams need to move from ticket-driven infrastructure delivery to product-style platform thinking. Infrastructure engineers, developers, security professionals, and compliance teams require shared ownership, standards, and visibility. Also, it’s essential for leaders to reward reuse and reliability, not just speed. Otherwise, every team builds its own automation, and drift rears its ugly head once again.

Another concern is compliance. Companies need strong policies in this area that include elements like clear separation of duties, evidence trails, approval logic, policy evaluation, exception handling, and continuous compliance checks.

Q: What common implementation mistakes do companies make that delay the timely and efficient adoption of IaC?

Bharadwaj: Several mistakes happen frequently. One is treating IaC as a tooling project rather than an operating model change. This mistake can lead to several drawbacks, such as copying bad manual processes into code and skipping module standards. Other potential impacts include failing to properly define state management and giving too much freedom too early. Additional common mistakes are pushing automation before teams have testing and governance in place and assuming IaC alone solves security. The reality is that the code also needs scanning, review, and policy enforcement to be truly secure.

Organizations can take several steps to avoid these missteps. First, they can maintain a strong focus on automation maturity by closely monitoring the extent of infrastructure codification and the consistency of policy checks in continuous integration/continuous delivery (CI/CD). Other steps include evaluating how often manual and automated changes occur and how recoverable the environments are. Teams can track performance through delivery metrics such as lead time, deployment frequency, change failure rate, and mean time to restore (MTTR).

Another best practice is to use key performance indicators (KPIs) to continuously measure the effectiveness of the IaC strategy. It is important to rely on both engineering and business KPIs. Engineering KPIs include change failure rate, deployment frequency, change lead time, MTTR, policy compliance rate, config drift incidents, environment provisioning time, and rollback success rate. Business KPIs include reduced operational effort, faster team onboarding, lower outage costs, faster product delivery, and fewer audit findings.

Q: How does artificial intelligence (AI) affect the growing utilization of IaC? 

Bharadwaj: The time when IaC infrastructure was managed through consoles and tickets has passed. Now, infrastructure is managed via code, pipelines, policies, and reusable platforms. This change affects how teams work and who owns delivery. Code-based standardization can feel restrictive to engineering teams, especially when platform governance is seen as slowing innovation. Companies can monitor this situation and take steps to acknowledge teams’ efforts and keep their morale high.

AI is another factor to consider. Advanced technology can accelerate authoring, but blind trust in AI-generated code can introduce hidden security, compliance, and maintainability issues. That’s why human oversight is critical. While AI improves speed, it’s imperative that governance, review, and accountability remain within the organization.

Manual Infrastructure Does Not Fail Just Because of Human Error

Manual infrastructure often fails because it does not scale in a controlled way. When used correctly, IaC enables more manageable scalability. Automation becomes less about tooling and more about engineering. It establishes ownership boundaries and governance models while unlocking greater reliability, auditability, and repeatable governance across the infrastructure.

Organizations that embrace IaC as a long-term operating model position themselves for success. These companies equip their IaC initiatives with platform standards and policy guardrails. They monitor measurable outcomes and ensure their IaC is more than just a collection of deployment scripts. Finally, they build their automation frameworks to survive changing technologies and regulatory requirements, providing a foundation for long-term growth and prosperity.

About the Author:

Ken Dare is a freelance writer with more than 20 years of experience covering technology topics, including infrastructure as code (IaC). For more information, contact writedare@gmail.com.

Disclaimer: The authors are completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.