3 reasons why AI in cloud is the better option against cyber attacks

BotX team
December 18, 2022
3 mins read

The days when organizations spent huge amounts of money on procuring and maintaining computing resources are disappearing. Today, more companies are adopting the subscription-based “as-a-Service” (XaaS) model of cloud computing.

XaaS enables firms to access cutting-edge resources and technologies at an affordable price – without having to buy, implement, or manage costly “on-premises” resources. One such technology is Artificial Intelligence (AI) which they can access under the AI-as-a-Service (AIaaS) umbrella.

AIaaS empowers companies to leverage advanced AIl functionalities from a third-party provider without building their own expensive AI systems. The model “democratizes” the AI landscape so more businesses of all sizes can use the technology for process automation, customer service, data analytics, cost estimation, social media monitoring, and many other applications.

But despite its many benefits, organizations must be cautious about one potential pitfall of AIaaS: security. This brief article will explore the security concerns of AIaaS. It will also compare the security of cloud-based and on-premises AI solutions so you can make an informed decision about choosing the most secure solution for your organization.

Security Concerns with Artificial Intelligence

Smart organizations recognize the benefits of AIaaS, which explains why one Flexera survey found that 37% of organizations are already using cloud-based AI technologies.Another 46% are either experimenting with or planning to use these technologies in the near future. Even so, security is a very real concern in AIaaS and withAI solutions in general.

All AI systems and machine learning (ML) models dependon significant amounts of data to work well and deliver accurate results. Amodel that is “trained” with good data can reveal relevant patterns that enableorganizations to make accurate predictions, initiate business improvements, andenhance decision-making.

However, these vast quantities of data also attract cyberattackers who can then steal it for their own nefarious purposes. They can alsocompromise enterprise accounts, hack into application program interfaces (APIs),and launch Denial-of-service (DoS) and other cyberattacks.

These threats can affect AI systems at any stage oftheir operation, from data collection and preparation to model training,inference, and deployment. Hackers can target these systems by poisoningtraining data, perturbing inputs, or capturing confidential inferences from trainingdata. The data collection and pre-processing phases are particularly vulnerableto sensor spoofing and scaling attacks. Data poisoning and corruption attacks,and adversarial attacks are common during the model training and inference-gatheringphases.

Some of the other common security threats that affectAI systems are:

·      System manipulation: An attacker inserts maliciousinputs into the system that causes the AI algorithm to make false predictions

·      Transfer learning attacks: Adversaries corrupt a task-specificML model to affect its results

·      Data extraction attacks: These attacks are hard to detect and put the entire AI system at risk

Security of AI On-Premises vs.Security of AI in the Cloud

To create better AI models and derive better-quality insights from their AIaaS solutions, companies share data with other parties, including the provider. If the data is not properly protected with robust storage, access, and transit controls, threat actors may be able to compromise it or tamper with the AI system and its models.

AI security is a particular concern for businesses inhighly regulated sectors like financial services and healthcare. Healthcare organizationsmust comply with the Health Insurance Portability and Accountability Act(HIPAA), while companies that handle consumers’ credit card information must followthe Payment Card Industry Data Security Standard (PCI DSS). These firms need toknow exactly how their AI data is secured. However, such knowledge is difficultto come by with AIaaS since the data is stored in the cloud and involves athird-party provider.

Fortunately, this is becoming less of a concern nowsince all major cloud providers now invest a lot of resources into securingtheir infrastructure and their cloud-based AI solutions. Most also offerservices that are aligned with various data privacy regulations.

Generally, organizations can better control thesecurity of their on-premises AI systems and decide what works best for them.Thus, they can install firewalls and antivirus software, set user accesspolicies, and implement security patches to guard against attacks and breaches.However, if these controls are missing or weak, these solutions are highlyvulnerable to security threats.

And missing or weak controls are often a result of ashortage of skilled cybersecurity experts. According to one report, between2013 and 2021, the number of unfilled cybersecurity jobs grew 3.5X from 1million positions to 3.5 million. These shortages prevent organizations fromadequately securing their on-prem AI systems, leaving them open to all kinds ofcyberattacks.

Finally, many organizations fail to set and followrobust security standards for their AI solutions. Such misses can create multiplesecurity blind spots that open the door to threats like data privacy attacks,model extraction, adversarial inputs, and training data poisoning.

The bottomline: there are security concerns with bothon-prem and AIaaS. But in general, cloud providers do all they can to securetheir infrastructure and protect their customers’ AI systems and data. Thiscreates a security advantage for organizations adopting AIaaS, which adds to itsmany other advantages, such as:

·      Cost-effectiveness

·      Access to advanced infrastructure without the hassleof infrastructure management

·      Flexibility to use resources on-demand

·      Full transparency into costs and resource utilization

·      Freedom to scale resources up or down as needed

The Security of BotX AIaaS Solutions

According to Gartner (as quoted by Microsoft), in2022, “30% of all cyberattacks will involve training-data poisoning, model theft, or adversarial samples to attack AI systems”. Clearly, there is a need to secure AI systems, both on-prem and in the cloud. And yet, Microsoft also found that a majority of businesses lack the tools to secure their AI and ML systems. This lack of preparation creates security gaps that allow threat actors to attack AI systems and cause serious damage to organizations.

To protect their data and business secrets, all companies need robust, security-focused AI solutions. Here’s where providerslike BotX come in. At BotX, we take the security of our AI products and services very seriously. We protect our systems by renting hardware servers from an ISO 27001-certified infrastructure vendor instead of relying on the “security of the cloud”approach of cloud providers like AWS or Google.

In addition, we have implemented all these security controls for the BotXAI platform and all custom AI solutions:

·      ISO/IEC 27001 practices to manage information security

·      TL 1.2 security protocol to securely authenticate and encrypt data transferred over the network

·      HTTPS protocol to secure all communications and datatransfers between web browsers and websites

·      Two-factor authentication (2FA) to minimize the risksassociated with password theft and compromise

·      JSON Web Token (JWT) to secure RESTful APIs and ensuresafe data transfers during two-party interactions

·      HMAC authentication to authenticate API calls, ensuredata integrity, and protect against man-in-the-middle (MitM) attacks

In addition, all BotX solutions are protected bymulti-layered security consisting of:

·      CDN firewall

·      Server firewall

·      Heuristics

·      Cryptographic layer

·      Cloudflare CDN and DDoS mitigation


By 2030, the size of the AIaaS market will reach USD 43.29 billion. AIaaS can bring huge benefits to organizations in all industries. Nonetheless, there is some room for improvement in AIaaS, particularly from a security perspective. BotX recognizes this, and that’s why all our offerings incorporate robust security measures to protect organizations and their business-criticalAI models and data. Click here to explore our products and solutions, or contact us for a free consulting session.

BotX team