The environmental impact of data centers includes energy use, water consumption, and land use, which operators address through efficiency, sustainability programs, and community engagement.
By: Jenny Gerson, Senior Director of Sustainability, DataBank
Many communities view data centers as large and loud neighbors they don’t want in their backyard. Among their claims: Given the square footage they take up, data centers don’t employ that many people, and municipalities could allocate the land for other uses. Some communities also see negative impacts on power and water pricing, and availability.
To address these concerns, data center companies need to engage in lengthy stakeholder discussions that include local governments and community representatives. Together, they can work on solutions that provide the area with the required computing resources while also limiting negative impacts on the environment and communities.
This includes discussions about energy consumption, water use, and carbon emissions. While the industry has made strides in sustainability —reducing environmental impact and increasing efficiency— forward-thinking operators now need to go beyond neutrality. They must embrace a net-positive approach that actively benefits the communities they serve and the environment at large.
Beyond Sustainability: The Shift to a Net-Positive Impact
Traditionally, sustainability efforts in data centers focused on minimizing harm: reducing carbon emissions, limiting water use, and improving energy efficiency. However, leading operators can take a more ambitious stance: Rather than simply offsetting their impact, they can invest in ways to create positive environmental and social contributions.
Here are a few examples of how data centers can lead the charge toward Net Zero emissions and other positive actions to protect the environment:
- Mitigate habitat impacts by making improvements such as planting trees and investing in conservation.
- Remove carbon from the air through direct air capture.
- Utilize cooling systems that don’t consume water.
- Use low-carbon or green materials such as concrete and steel, investing in local, time-matched renewable power.
- Sponsor projects that improve water availability and quality by generating more renewable power than needed and deliver excess to the grid.
- Operate as a virtual power plant (VPP). A DataBank data center in Irvine operates as a VPP. This approach enables batteries to charge during times of excess renewable generation and discharge during peak energy demand hours. This also provides an alternative to utilities having to ramp up dirty peaker power plants.
Waste Heat and Renewable Energy Innovations
One of the most straightforward, net zero initiatives in the industry is the repurposing of waste heat—excess thermal energy generated by servers. This includes converting flared methane from oil wells to electricity.
By cycling waste heat, data centers can operate as assets to their communities rather than liabilities. This shift is not just about corporate responsibility; it’s a strategic response to increasing regulations, stakeholder expectations, and the demand for environmentally conscious IT infrastructure.
Instead of venting this heat into the atmosphere, data centers can warm homes, businesses, and even greenhouses. This requires infrastructure to connect a data center to a nearby building or add it to a district heating system.
For example, data centers in Scandinavia and other parts of Europe already use this approach by connecting their infrastructure to district heating systems. This significantly reduces the demand for fossil fuels in residential areas.
This kind of innovation, which DataBank delivers in one of its Atlanta data centers, transforms data centers into contributors to local energy ecosystems rather than consumers of massive amounts of power. This approach is much easier to deploy in Europe, where data centers rely on district heating. The US largely does not, so the use-case is not as strong—unless nearby buildings can easily accept the waste heat.
Additionally, some operators invest directly in renewable energy projects, not just to offset their own consumption but also to stabilize and expand clean energy for entire regions. By partnering with utilities, data center operators can provide excess power during peak production hours and reduce the strain on local grids while enhancing overall energy security.
Water and Resource Conservation
Water usage remains a key sustainability challenge for data centers, particularly those operating in drought-prone regions. To ensure they do no harm, some facilities implement advanced cooling systems that minimize water dependency.
This includes air and liquid cooling advancements that reduce the need for traditional water-cooled systems. Another tactic involves AI-driven resource optimization to adjust cooling strategies dynamically and ensure efficient operations with minimal environmental impact.
By prioritizing water conservation and innovative cooling strategies, data centers can significantly reduce their impact on local water resources while maintaining operational efficiency.
Community Investment and Economic Growth
As demand for digital infrastructure grows across the US, leading data center operators understand their impact on surrounding communities. This is why many focus on opportunities to invest in local community initiatives such as workforce development, power and connectivity improvements, and road and utility updates. In addition, data centers bring significant property and business tax revenue, which can fund schools, municipal improvements, and other public services.
Leading operators also launch STEM education programs in regions where they build facilities. In some cases, they provide training and career pathways in technology, engineering, and data center management for higher-ed students to sustain the growing demand for the future of AI and cloud infrastructures.
Others work closely with local governments to develop workforce training programs that prepare residents for high-paying, skilled jobs in data center operations, security, and engineering. Some data centers also partner with local nonprofits through grants, sponsorships, and donations—further providing critical assistance to surrounding communities.
Moreover, data centers can focus on engaging with industry infrastructure partnerships to enhance regional connectivity by investing in fiber networks, smart grid technologies, and emergency response systems. This not only benefits data center customers but also improves digital access for surrounding communities.
The Future of Net-Positive Data Centers
While achieving a truly net-positive status requires long-term investment and collaboration, the industry is making significant strides. By focusing on renewable energy, resource efficiency, community engagement, and regulatory alignment, data centers can prove they are more than just power-hungry infrastructure; they are drivers of positive change.
As enterprises evaluate their IT infrastructure partners, choosing data centers that prioritize these initiatives will reduce their own environmental impact and contribute to broader sustainability goals. The future of data centers is not just about doing less harm; it’s about creating more good.
By: Chris Chandler, Solutions Engineer, DataBank
Digitized data is more critical than ever to the successful operation of businesses. Companies need data to drive operations, serve customers, and remain competitive. With threats such as cyberattacks, natural disasters, and hardware failures, businesses must implement data-loss mitigation solutions to ensure continued business success.
Two popular solutions for protecting data are Disaster Recovery as a Service (DRaaS) and Traditional Backups. While both are primarily used to protect data, they differ significantly in functionality, cost, and speed of recovery. Understanding these differences is important when choosing the right solution for your business.
What is Traditional Backup?
Traditional backup involves regularly copying data to a storage medium or cloud storage service. The primary goal is to create a copy of data that can be restored in case of accidental deletion, corruption, infection or system failure. Traditional backups can be performed using various methods, including:
- Full Backup: Copies all selected data at once, ensuring a complete restore point.
- Incremental Backup: Saves only the changes made since the last backup, reducing storage needs and backup time.
- Differential Backup: Similar to incremental backup, but it saves all changes since the last full backup, requiring more space but faster recovery than incremental backups.
Businesses often use backup software or hardware appliances to automate backup processes. These backups can be stored onsite (local backup) or offsite (cloud or tape backup) to protect against physical damage or theft. There are also additional options of storing the backups in immutable storage, which protects specifically against encryption from Ransomware attacks.
Removable media is also an option where the media the backups are stored on are removed periodically from the Production Environment. This adds a layer of protection against internal bad-actors or people physically stealing or altering data.
What is DRaaS?
Disaster Recovery as a Service (DRaaS) is a cloud-based solution that provides businesses with a comprehensive disaster recovery plan. Unlike traditional backups, which focus solely on data storage, DRaaS replicates an entire IT infrastructure, including applications, servers, and configurations. In the event of a disaster, businesses can quickly switch to the replicated environment with minimal downtime.
Differences Between DRaaS and Traditional Backups
Recovery Speed and Business Continuity
One of the most significant differences between DRaaS and traditional backups is the speed of recovery.
- Traditional Backup: Restoring data from backups can be a time-consuming process, especially if large volumes of data are involved. If backups are stored offsite, retrieval can take hours or even days, leading to extended downtime.
- DRaaS: Databank offers near-instant recovery by switching operations to a replicated cloud DR Site. Customers can maintain productivity with minimal disruption, making DRaaS a required choice for businesses that cannot afford downtime.
Management
- Traditional Backup: Requires businesses to manage backup schedules, ensure proper storage management, and handle restoration manually when needed.
- DRaaS: Is automated and managed by Databank. Our DRaaS solutions include annual failover testing, automated updates, and real-time replication to ensure seamless recovery.
Cost
- Traditional Backup: Typically has lower initial costs since businesses can have differing needs for data retention and storage type and locations. Costs can add up quickly depending on needs.
- DRaaS: Databank DRaaS is often much more cost-effective than traditional backups as the compute resources are reserved and not used unless a disaster is declared by the customer. This lowers power consumption and resource utilization, helping keep costs down.
Scalability
- Traditional Backup: Scaling backup storage requires additional investments in hardware or cloud storage, which may not be as flexible.
- DRaaS: Databank offers essentially limitless scalability, allowing businesses to adjust resources as needed without major capital expenditures. It also supports hybrid environments, enabling integration with existing On-Prem or collocated IT environments.
Which is Best for Your Business?
Choosing between DRaaS and traditional backup depends on your business needs, budget, and recovery objectives. Here are some items to consider:
When to Choose Traditional Backup
- You have non-critical applications that can tolerate some downtime.
- You have an in-house IT team that can manage backup and restoration processes.
- Your budget is limited, and you prefer a more cost-effective solution.
When to Choose Databank DRaaS
- Your business requires high availability and minimal downtime.
- You run mission-critical applications that need rapid recovery in case of failure.
- You want a fully managed disaster recovery solution without adding staff, capital expenditure or increased workload.
Final Thoughts from the Solutions Engineer
DRaaS and traditional backups have important, but distinctly different roles in data protection. Traditional backup offers a straightforward and cost-effective way to store data, Databank DRaaS provides a quicker and more functional recovery solution. Businesses must assess their risk tolerance, operational requirements, and budget to determine the proper solution.
The Hybrid Approach: (My recommendation)
Most businesses will adopt a hybrid approach, combining both traditional backup and DRaaS for layered protection.
This means using traditional backups for regular data archiving while leveraging DRaaS for business-critical services.
At the end of the day, backups and DR should be recognized for what they are; technical insurance against significant data-loss and/or lost revenue due to a major IT systems outage.
Choose your coverage appropriately.
By Dan Fuentes, Senior Vice President of Enterprise Sales
This valued customer is a small east coast-based software developer of tools to manage the largest, most complex multimedia datasets for law enforcement and national security customers in the United States and abroad.
BUSINESS PROBLEM
The customer was looking for a way to host and manage its critical IT infrastructure to increase its overall visibility and control over its IT assets.
As a smaller company, it lacked the budget needed to modernize and expand its facilities to achieve these goals, especially when it came to space, power, and networking capabilities. Similarly, the company had a small, but busy IT team, that didn’t have the time to stay on top of evolving security and data protection best practices.
Still, the company wanted to pursue a geographically diverse colocation strategy because this would be consistent with support uptime, disaster recovery, and company growth goals. The company also wanted to select a partner who could provide the widest variety of infrastructure hosting options and services, including colocation, cloud, and interconnections.
DATABANK SOLUTION
After moving their IT infrastructure to a DataBank data center, the customer saw improvements in the overall performance, agility, and scalability of its computing systems.
The company also gained real-time visibility into all IT assets. This alone has been a significant edge in improving its overall control over business-critical applications and systems as well as understanding and minimizing potential risk.
This software developer was also able to free its IT staff from the burden once associated with managing IT systems and can now focus on higher-value business objectives.
BUSINESS RESULT
Partnering with DataBank helped the customer achieve the following results:
•Gain access to multiple carriers and a larger interconnection ecosystem
•Monitor and control IT infrastructure remotely
•Rely on the DataBank services team for migration assistance and 24×7×365 technical help
•Improve their ability to achieve compliance with industry regulations such as FedRAMP, HIPAA, SOC, PCI-DSS, and GDPR
According to our customer, DataBank took the time to understand its business, respond to specific needs, and manage mission-critical IT infrastructure.
It’s what we do every day.
Ethical AI focuses on using artificial intelligence responsibly by addressing data privacy, security, transparency, and potential bias in automated systems.
Ethical AI: Balancing Innovation and Security
By Mark A. Houpt, Chief Information Security Officer and Jenny Gerson, Head of Sustainability & Corporate Responsibility – DataBank
Artificial intelligence is quickly transforming how businesses operate today. Powerful new AI tools have the potential to transform many business processes to enhance productivity, automate workflows, and generate valuable new insights.
At the same time, these AI tools and their many uses also present new and unpredictable risks. As a result, many organizations are currently wrestling with how to encourage and enable ethical AI adoption while making sure it remains secure and completely aligned with corporate risk management and governance policies.
The Security Dilemma: Enabling AI While Managing Risk
So many security leaders and professionals are skeptical about AI’s role in their organizations. This is not because they oppose innovation – especially when it’s proven to deliver so many benefits – but because there are simply too many unknowns related to its use.
Put another way: Security teams must do all they can to enable the business to use AI tools in a way that is as risk-averse and secure as possible. Still, the unknown and quickly changing aspect of AI is a security risk unto itself.
While AI tools such as ChatGPT and Microsoft Copilot can now provide a seemingly infinite range of new benefits, they also raise security concerns. Without the proper controls, these tools could inadvertently access and/or expose the organization’s most sensitive information.
For example, consider the case where a corporate attorney uses Microsoft Copilot to draft a summary of ongoing litigation for an internal briefing. Because Copilot is trained to find as much relevant information as possible, it scans the company’s emails, internal memos, and various legal documents stored in SharePoint to compile key details.
However, without strict access controls, this search includes privileged attorney-client communications and other highly sensitive information that was not properly secured. Copilot pulled these results into its summary document without the attorney realizing how sensitive this data was. Worse, the financial information was injected into Copilot’s public large language model (LLM) during an external source, resulting in the permanent loss of control of the information within the AI infrastructure.
The Challenges of Data Classification and Access Controls
Many organizations – especially those outside of highly regulated industries – still lack strong, comprehensive data classification policies, or have yet to implement them effectively. In contrast, government agencies and companies in highly regulated sectors have established processes and frameworks to help manage sensitive data.
As AI tools continue to proliferate and expand, organizations in all industries must now take a closer look at their approach to data security and governance.
More specifically, this now means Chief Information Security Officers (CISOs) and other security leaders should ask these questions:
- What data is the AI tool accessing and compiling in response to various requests?
- What might it share without our knowledge?
- Where does this data go once it’s processed?
These concerns are more than theoretical possibilities since it’s likely the use of AI could lead to unintentional data leakages, breaches, and other adverse outcomes.
The Risk of Unintentional Data Exposure
Security teams must carefully consider how AI interacts with structured and unstructured data. Another example: Imagine the scenario where a marketing manager uses ChatGPT to help conduct a detailed market analysis report that includes the top 500 potential buyers of its new product.
Without the right security controls in place, ChatGPT may also pull external information that has been loaded into the LLM, including sensitive purchase histories, contract values and even personally identifiable information (PII).
Without realizing it, the marketing manager includes these AI-generated insights into the report, and in doing so, potentially exposes confidential customer data to unintended recipients. Not only does this violate internal security policies and present compliance concerns, but it also shares information about the company’s product and go-to-market strategies.
Here’s one more example. A healthcare company uses an AI tool to analyze various demographic data to begin determining how many individuals could be susceptible to a particular disease or condition. If sensitive information is not properly detected, it could result in the loss of data, representing a significant HIPAA compliance violation that exposes patients’ health and PII data and presenting the organization with very real legal issues and possible fines.
Ethical AI and Governance Challenges: Is AI Eating its Own Tail?
Beyond the immediate security risks, AI also presents a deeper challenge: governance and integrity. As businesses rush to adopt AI-driven tools, they must consider not just what these systems can do, but also how reliable their outputs really are.
One of the growing concerns in the AI space is that AI models are increasingly trained on AI-generated content, creating a kind of feedback loop – sometimes referred to as AI “eating its own tail.” The Atlantic recently explored this phenomenon in its article, “AI Is an Existential Threat to Itself,” highlighting the risk that future iterations of AI models will be trained AI-generated or poor-quality data. It’s the classic example of “garbage in, garbage out,” raising concerns about the accuracy and quality of AI results.
This raises a fundamental governance issue: How do organizations ensure that AI remains grounded in factual, high-quality information rather than recycling and amplifying any inaccuracies? If a significant portion of an AI model’s training data originates from other AI systems, it risks reinforcing biases, misinformation, and degraded content quality over time.
While AI developers don’t always disclose the exact sources of their training data, it’s a valid concern. Consider ChatGPT, which (as of now) has built-in knowledge only through 2021. If an enterprise relies on such a system without access to more recent data, is it truly a reliable platform for making critical business decisions?
Also, if the next versions of AI models are trained primarily on AI-created content, poor-quality information, or worse, deliberately infiltrated by a nation-state threat actor, how will that impact the integrity of the outputs?
From a governance perspective, companies need to take proactive steps:
- Audit AI-generated outputs: Validate key insights rather than taking them at face value.
- Set data sourcing policies: Ensure AI tools are trained on verifiable, high-quality, and up-to-date information.
- Maintain human oversight: AI should assist, not replace, human decision-making, especially in critical areas like security, compliance, and risk management.
Governance is a core pillar of corporate responsibility, and AI must be held to the same standards as any other business initiative. Businesses that fail to address these issues may find themselves making strategic decisions based on AI-generated hallucinations – an outcome no CISO or governance executive wants to face.
Balancing Innovation and Responsibility
AI’s potential is undeniable, but its risks – both security related and ethical – cannot be ignored. Security leaders and organizations must recognize that AI is not just another “set-it-and-forget-it” tool. It requires carefully designed governance, continuous oversight, and a commitment to responsible adoption.
Striking the right balance means embracing AI’s advantages while mitigating its vulnerabilities, ensuring that innovation does not come at the cost of security, compliance, or trust. In an era where AI is shaping the future of business, those who proactively manage these challenges will be best positioned to lead with confidence.