The upcoming U.S. national elections are generating a lot of attention, and with it, an increased number of election-related cybersecurity risks. In fact, the Cybersecurity and Infrastructure Agency (CISA) has officially rolled out its #Protect2024 program. This initiative offers guidance and best practices to help organizations improve the physical security and the cybersecurity of the many systems that support our elections.
How Election Threats Could Affect Data Centers
Data centers today have many different clients using shared infrastructure and services. This makes them attractive targets for threat actors aiming to exploit vulnerabilities in one company’s system and then access so many others.
For example, an election-related cyberattack such as a distributed denial-of-service (DDoS) attack against one client could affect all the other tenants in the same hosting environment. Such an attack could lead to disastrous results: downtime, data loss, or the direct compromise of sensitive corporate systems.
Additionally, election-targeted attacks often involve social engineering cyberthreats. Consider the case where a corporate employee falls victim to a sophisticated phishing attack. Not only could the hackers access that employee’s credentials, but they may exfiltrate data or move laterally to gain access to other systems in the same facility or on the same network.
Third-party service providers within the data center ecosystem also pose significant security risks. During periods of heightened election-related threats, attackers may target these vendors as weak entry points. If successful, they can exploit any vulnerabilities to infiltrate broader networks, disrupt services, or access sensitive corporate and election infrastructure.
With so much risk, companies need to do all they can to bolster their defenses. They also need to make sure their data center partner clearly explains their role, responsibilities, and can deliver effective cybersecurity services and solutions to mitigate the risk of cyberattacks in November – and beyond.
Mitigation Strategies for Businesses Amid Election-Related Cybersecurity Risks
As election-related cybersecurity risks escalate, businesses operating in colocation data centers must implement strong security strategies to safeguard their systems.
Here are key approaches companies can adopt:
- Strengthening access controls: One of the most effective ways to mitigate threats is by ensuring robust access control policies. Businesses should collaborate with their data center providers to limit physical and digital access to sensitive infrastructure. This includes using multi-factor authentication (MFA) role-based access controls and limiting access to only essential personnel. This helps reduce the risk of unauthorized entry or exploitation.
- Enhanced monitoring and threat detection: Advanced monitoring tools are crucial in detecting abnormal activities or potential threats in shared environments. Data centers often offer security monitoring services that provide real-time visibility into the network. These tools can flag unusual behavior, such as sudden spikes in traffic or unauthorized access attempts. As a result, businesses can respond swiftly and prevent escalation.
- Regular security audits and penetration testing: Routine security audits and penetration testing are essential to uncovering hidden vulnerabilities before they can be exploited. By partnering with multi-tenant data center providers, businesses can ensure regular assessments are conducted. If any vulnerabilities are detected, they can be addressed promptly. Such a proactive approach strengthens the overall security posture.
- Collaboration with data center providers: Open communication between businesses and data center providers is critical. Keeping both parties informed of emerging threats and working together to refine response plans ensures a coordinated defense against election-related cyberattacks.
- Business continuity planning: Finally, businesses should review and update their business continuity and disaster recovery plans. Tailoring these plans to the specific risks posed by election-related threats will enable companies to maintain operations in the event of a disruption. It also ensures minimal impact on business-critical processes.
By focusing on these strategies, businesses can better navigate the heightened security risks associated with the upcoming election and ensure their systems remain secure.
Role of Multi-Tenant Data Centers in Mitigation
Multi-tenant data centers play a crucial role in helping businesses safeguard their infrastructure. Providers like DataBank offer a suite of security measures and compliance standards that help mitigate these risks.
- Security measures offered by providers: Multi-tenant data centers provide a range of advanced security features to protect against cyberattacks. DataBank, for example, offers DDoS protection, network-level intrusion detection and prevention systems (ID/PS), enhanced firewalls, and comprehensive security monitoring services. These tools are essential in detecting and mitigating threats that can disrupt election-related systems and corporate infrastructure. By leveraging these services, businesses can secure their digital environments and minimize the risk of a breach during vulnerable times.
- Compliance and certifications: Choosing a data center provider that adheres to rigorous security standards is vital for reducing risks. DataBank complies with industry-leading certifications, including SOC 2, FISMA/NIST, and ISO 27001, which ensure robust data protection protocols are in place. These certifications verify that DataBank maintains stringent controls over data integrity, confidentiality, and availability. In times of heightened threat, these extra layers of protection are invaluable, offering businesses peace of mind knowing their infrastructure is compliant with critical security standards.
Interested in learning more about DataBank and our approach to helping our clients improve their security defenses? Visit our managed security services page or contact us today for more information.
By: Lissa Harr, senior director of talent acquisition, DataBank
The Challenges of Interviewing Technical Talent
Given the role technology plays in today’s digital economy, finding the right technical talent can make or break success for most organizations. When it comes to the interview process, hiring managers face many unique challenges in making sure candidates fit in well:
- Technical professionals often possess specialized skills difficult to assess through traditional interview methods.
- Technical jargon and the need to evaluate hard and soft skills further complicates the process of assessing expertise.
- The need to determine whether candidates align with the company culture.
It takes a carefully planned strategy to effectively extract critical insights during interviews to ensure hiring decisions sync with organizational goals and values. In this blog, we examine five common challenges when interviewing technical candidates. We also provide tips to overcome those challenges. With this information, you can equip your hiring managers, recruiters, and interviewers with the tools to confidently assess applicants and make sure they will prove valuable for your company.
Interview Challenge #1 – Communication Barriers
A significant obstacle when interviewing technical professionals is the communication gap. If the interviewer isn’t well-versed in technical terms or processes, candidates might struggle to explain complex concepts in simple terms.
Candidates might also focus on niche technical details that don’t convey their problem-solving abilities. This misalignment can lead to underestimating or overestimating candidate skills. Hiring managers may find it difficult to gauge their suitability for the intended role.
To overcome this challenge, collaborate with technical team members. Ask them to review resumes, and bring them to interviews to bridge the communication gap. Involving technical colleagues in the process allows for more meaningful conversations about skillsets and experience.
Also, structure technical and non-technical questions separately. To ensure well-rounded evaluations, separate interviews into technical and behavioral sections. This allows you to focus on different aspects of candidate capabilities without overlapping or creating confusion. By ensuring the interview team has a mix of technical and non-technical experts, you can better interpret candidate responses and extract critical insights into their qualifications.
Interview Challenge #2 – Assessing Hard Skills
While it’s important to evaluate technical expertise, too much focus on hard skills leads to overly complicated interviews. It’s also easy to fall into the trap of relying solely on technical tests or coding challenges. These assessments might not fully reflect practical abilities and teamwork skills.
For this challenge, use real-world problem scenarios. Rather than focusing on abstract technical questions, present candidates with real-world problems. This not only assesses their technical skills but also their problem-solving approach—and their ability to handle practical challenges.
Another effective tool is a structured technical test. This focuses on the core competencies for the role. You can evaluate whether candidates arrive at correct answers and their process for solving problems.
This strategy provides a holistic understanding of their technical strengths. You will also know how they might perform in real-world work environments.
Interview Challenge #3 – Evaluating Cultural Fit
In technical interviews, hiring managers often focus on assessing hard skills. This leaves too little time for evaluating soft skills and cultural fit. Beware, this approach is risky. Technical talent needs to work within the broader company culture. Hires also must collaborate effectively with technical and non-technical team members.
To evaluate cultural fit, incorporate behavioral questions that reveal how candidates work within a team, respond to challenges, and fit with organizational values. For example, ask about a time the candidate dealt with a difficult team member or handled an unexpected problem under pressure.
Soft skill evaluations should assess communication, collaboration, and adaptability. Ask open-ended questions and observe responses in both technical and non-technical discussions.
Finding a candidate who fits into your company culture is as important as finding someone with the right technical expertise. Including soft skills in the interview process ensures hires will integrate smoothly into your team and contribute positively to your company.
Interview Challenge #4 – Overcoming Bias
Unconscious bias can creep into any hiring process. Technical interviews are no exception. Whether it’s an assumption based on a candidate’s educational background, communication style, or previous employers, biases cloud judgment and lead to poor hiring decisions.
You can overcome bias with a structured interview process that asks the same set of questions for all candidates. This ensures you evaluate each candidate based on his or her responses, rather than on preconceived notions.
If possible, use blind technical assessments. The interviewer only sees the work—not the candidate’s resume and background. This focuses attention on the quality of his or her technical skills rather than external factors. By minimizing bias, you can make more objective hiring decisions based on a candidate’s qualifications and fit for the role.
Interview Challenge #5 – Extracting Critical Insights
Technical interviews sometimes feel rushed. The sessions may focus too much on knowledge of specific tools or technologies. This narrow approach can lead to missing critical insights into how a candidate thinks, solves problems, and adapts to challenges.
To extract critical insights, ask probing, open-ended questions. To gain deep knowledge into a candidate’s thought process, you need more than a simple Yes or No answer.
For instance, ask candidates to walk you through how they handled obstacles on a challenging project. This gives you a window into their decision-making, problem-solving approach, and their level of ownership over their work.
Remember to evaluate learning ability. The ability to adapt is often more valuable than knowledge of a technology. Ask candidates how they stay updated with industry trends and how they approach learning new technologies. These questions reveal more about a candidate’s suitability for the role than just their technical knowledge.
Make the Right Hiring Decisions
Interviewing technical talent requires a balance of assessing both technical expertise and soft skills while overcoming communication challenges and minimizing bias. By using structured interviews, involving technical team members, and focusing on real-world problem-solving abilities, hiring managers can capture the critical insights necessary to make informed hiring decisions.
With these tips in mind, we encourage you to evaluate your technical interview process. To make the right hiring decisions, consider adopting these strategies to improve your ability to attract and select the right technical talent. We also invite you to check out the resources below for more information on hiring technical talent.
By Anthony Mares, Director of Infrastructure Engineering, DataBank
AI at the edge refers to running artificial intelligence models directly on local devices or nearby servers rather than sending data to a distant cloud. This matters enormously when a split-second response is non-negotiable: think autonomous vehicles reacting to a pedestrian, a smart factory camera flagging a defect on the assembly line, or a hospital monitor detecting an irregular heartbeat. Traditional cloud AI introduces latency because data must travel to a central server and back. Edge AI eliminates that round trip, processing data right where it’s generated. For US businesses in manufacturing, healthcare, retail, and logistics, edge AI is already delivering measurable gains — reducing equipment downtime through predictive maintenance, cutting shrinkage through real-time inventory monitoring, and improving patient outcomes through continuous vitals analysis. Edge AI also reduces bandwidth costs and strengthens data privacy since sensitive information stays on-site rather than traveling over public networks. As 5G connectivity expands across American cities, edge AI deployments are accelerating rapidly, making it one of the most transformative infrastructure trends of the decade.
AI at the Edge
As AI’s popularity grows and is proving to deliver innovative new business benefits, we’re seeing many cases where it can be combined with other technologies. AI-powered analytics. AI-driven virtual assistants. AI in industry-specific technologies in healthcare, finance, insurance, and virtually every other sector.
What about AI at the edge, though? Just what is this combination, and what does it mean for companies looking to take advantage of all it can offer?
AI at the edge refers to the deployment of artificial intelligence algorithms on edge devices, servers, or systems directly at the “edge” of a network instead of relying solely on centralized processing. This approach enables real-time data analysis and decision-making closer to where the data is generated, helping to reduce latency and improve application performance.
Initially, AI models required extensive resources and were run on powerful servers in centralized locations. However, as devices became more capable and the demand for immediate insights grew, the shift to edge computing emerged, enabling AI to operate independently in more diverse and dynamic edge environments.
This has opened new possibilities for industries where real-time responses are critical such as technology, healthcare, finance, automotive, and utilities. Some companies are already utilizing AI to screen resumes and provide HR support to their employees.
The Benefits of AI at the Edge
AI in edge computing enhances the efficiency, speed, and intelligence of localized data processing. These AI algorithms can perform various tasks such as data analysis, pattern recognition, decision-making, and predictive modeling – all at the source of the data.
Here’s a closer look at how AI functions within edge computing in various industries and its overall benefits:
- Real-time data processing: AI at the edge allows for the immediate processing of data where it is generated. For example, in a smart manufacturing plant, AI algorithms embedded in edge devices can analyze data from machinery in real time, detecting anomalies or predicting equipment failures. This rapid processing enables immediate corrective actions, minimizing downtime and maintaining production efficiency.
- Reducing latency: By processing data locally, AI at the edge significantly reduces the time it takes to transmit data to a central server or cloud for analysis and back. This is crucial in examples such as autonomous vehicles, where split-second decisions are necessary.
- Optimizing bandwidth use: AI at the edge helps to optimize network bandwidth by processing and filtering data locally, sending only relevant or summarized information to the cloud when necessary. For example, in a video surveillance system, AI can analyze footage at the edge to detect unusual activity, sending alerts and relevant clips to the central system rather than streaming all footage continuously.
- Enabling more intelligent decision-making: Edge AI enables devices to make intelligent decisions autonomously without relying on cloud-based instructions. For instance, in agriculture, AI-equipped drones can fly over fields, using edge computing to analyze soil conditions and plant health in real time. Based on the analysis, the drone can make immediate decisions, such as where to apply fertilizer or water, optimizing resource use and improving crop yields.
- Enhancing security and privacy: Processing sensitive data locally on edge devices reduces the need to transmit it to external servers, thus minimizing the risk of data breaches. In healthcare, for example, AI at the edge can analyze patient data directly on medical devices, providing immediate insights to physicians and clinicians while keeping the data within the hospital’s secure environment.
How Data Centers Can Provide AI at the Edge
Data centers are now doing all they can to offer AI at the edge to meet the growing demand for low-latency, high-performance applications across various industries. As more businesses adopt IoT, autonomous systems, and real-time analytics, the need for immediate data processing close to the source has become critical.
Offering edge AI capabilities enables data center companies to differentiate themselves in a competitive market. By integrating AI at the edge into their services, data center companies can provide customers with the ability to process and analyze data locally, reducing the time it takes to make decisions and improving the responsiveness of their applications. Utilizing high performance computing (HPC) clusters, which are made up of multiple servers connected through high-speed networks, allows for parallel processing. For their customers, this means enhanced performance where milliseconds can make a significant difference.
AI at the Edge Use Cases
Gaming
Consider the case of a popular online gaming company that has millions of players across the globe, including in regions with varying levels of internet infrastructure. To ensure that every player, regardless of location, experiences smooth gameplay with minimal latency, the company leverages AI at the edge for its game hosting.
For example, in a densely populated city on the west coast, where internet speeds can fluctuate due to high user demand, the company deploys edge servers closer to this specific geographic area. These servers host the game locally, allowing players to connect to a nearby data center instead of a distant central server. The edge AI continuously monitors network conditions, predicting and adjusting for potential slowdowns in real-time. This ensures that players enjoy fast, responsive gameplay with reduced lag, even during peak hours.
By hosting the application closer to consumers, the gaming company enhances the overall player experience, providing a competitive edge in markets where internet connectivity may not be as robust. This approach also allows the company to roll out updates and new features rapidly in specific regions, tailoring the experience to local preferences and maintaining high performance globally.
Smart Cities
Or imagine the example of a smart city where the energy grid is optimized using edge AI. Smart meters in homes and businesses continuously monitor energy consumption and weather conditions, making real-time adjustments to energy distribution.
During a sudden heatwave, the edge AI in these meters can quickly decide to balance the load, diverting excess energy from non-essential areas to critical cooling systems in hospitals or other vital facilities. By processing this information locally, the city ensures a stable energy supply, even in rapidly changing conditions, demonstrating the transformative power of AI at the edge.
AI at the Edge Delivers Better Results
By bringing AI closer to where data is generated, edge computing becomes faster, more responsive, and more efficient. This combination not only improves performance in applications requiring real-time processing but also enables smarter, more autonomous systems across various industries.
AI risks for data security
One thing is true when it comes to cyber threats: The bad guys will continue to do all they can to stay a step ahead of those trying to defend against cyberattacks. We can expect cybercriminals to use all tools at their disposal, including AI. We are now seeing new cases of more sophisticated AI techniques being used in data breaches and other attacks.
These can include the use of AI to create highly personalized emails or other communications in phishing attacks. These also include AI-generated deepfake videos that appear to be from a company’s CEO and instruct employees to take a specific action that could increase the vulnerability. Other examples include AI-enabled ransomware that can learn as it goes to adapt and modify ransomware files over time, making them more difficult to detect.
Just consider these ripped-from-the-headlines examples:
Unfortunately, today’s business leaders anticipate that the problem will only get worse. A recent survey found that 85% of cybersecurity leaders believed that their most recent attacks were powered by AI. The same survey found that 46% of respondents believed generative AI will leave businesses more vulnerable to cyber attacks than they were before the days of widespread AI use.
How Can AI Be Used in Cyber Threats?
The increased adoption and use of AI is clearly leading to new data security concerns due to its ability to process vast amounts of information, often from many different sources, at unprecedented speeds. In turn, this capacity raises new risks of unauthorized access to confidential data and the potential misuse sensitive data. This is especially true, given that AI models can be used to identify previously undetected vulnerabilities or extract patterns in personal information.
It can add up to a situation where bad actors have the advantage. For example, cyber criminals might use AI to automate cyberattacks and quickly increase their scale, making it much more difficult for traditional cybersecurity systems to keep up. As described in the Activision example above, AI can make extremely sophisticated – and convincing – deepfake videos and other assets used in phishing campaigns.
All of this represents a significant change in the overall landscape of security threats and makes it difficult for cybersecurity leaders and even company employees to stay a step ahead.
Best Practices to Protect Against AI-Related Data Security Concerns
Companies can best protect themselves against these new AI-related data security concerns by following these best practices.
- Conduct regular security audits: This includes performing routine security assessments and audits on AI systems and existing data-handling processes to identify vulnerabilities and ensure compliance with data protection regulations.
- Implement strong data encryption: Ensure that all sensitive data, both at rest and in transit, is encrypted using advanced encryption protocols to prevent unauthorized access during AI processing.
- Monitor AI systems for unusual behavior: Implement continuous monitoring and anomaly detection to identify any suspicious behavior in AI models, such as potential data poisoning or unintended model drift.
- Adopt robust access controls: Restrict access to sensitive data and AI systems based on user roles, enforcing multi-factor authentication (MFA) and the principle of least privilege (POLP) to limit exposure.
- Ensure data anonymization: Remove or anonymize personally identifiable information (PII) before AI training to minimize the risk of data breaches or privacy violations.
- Stay up to date with regulations: Regularly review and update internal data protection policies to stay in line with evolving laws like Data Privacy Framework, CCPA, and industry standards for AI ethics and data security.
Effectively safeguarding against AI-driven cyber threats requires a proactive and multi-layered approach to security that evolves alongside emerging risks. One solution? Partnering with a data center provider offering managed security services can further strengthen these efforts by ensuring continuous monitoring, advanced threat detection, and tailored security solutions.
How Data Center Partners Can Make the Difference
A data center partner that offers managed security services can play a crucial role in helping companies address AI-related data security concerns. By providing specialized expertise in data protection and system monitoring – as well as specialized security services such as web application firewalls, vulnerability scanning, and multi-factor authentication – data center operators can help make sure the infrastructure supporting AI systems remains secure.
With a comprehensive portfolio of managed security services, data centers real-time monitoring and threat detection, using advanced tools to quickly identify and mitigate vulnerabilities. This can be especially important for AI, where malicious actors may try to exploit weak points in data storage or processing.
Additionally, a managed security partner can help enforce strong encryption protocols, access controls, and ensure that data is stored and transferred securely within the data center. They may offer automated tools that keep track of who is accessing sensitive data and ensure compliance with regulatory frameworks. This is critical as companies navigate the complexity of securing large datasets often used for AI training, especially when those datasets may contain sensitive or personally identifiable information.
Data center partners can also support companies with regular security audits and updates and can proactive approach to detecting potential vulnerabilities or threats in the AI models and data flows. By handling the complexity of security management, they free up internal teams to focus on further security innovation while ensuring that data security and privacy standards are upheld at every stage.