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 depend on significant amounts of data to work well and deliver accurate results. A model that is “trained” with good data can reveal relevant patterns that enable organizations to make accurate predictions, initiate business improvements, and enhance decision-making.
However, these vast quantities of data also attract cyber attackers who can then steal it for their own nefarious purposes. They can also compromise 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 of their operation, from data collection and preparation to model training, inference, and deployment. Hackers can target these systems by poisoning training data, perturbing inputs, or capturing confidential inferences from training data. The data collection and pre-processing phases are particularly vulnerable to sensor spoofing and scaling attacks. Data poisoning and corruption attacks, and adversarial attacks are common during the model training and inference-gathering phases.
Some of the other common security threats that affectAI systems are:
· System manipulation: An attacker inserts malicious inputs 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
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.
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 in highly regulated sectors like financial services and healthcare. Healthcare organizations must comply with the Health Insurance Portability and Accountability Act(HIPAA), while companies that handle consumers’ credit card information must follow the Payment Card Industry Data Security Standard (PCI DSS). These firms need to know exactly how their AI data is secured. However, such knowledge is difficult to come by with AIaaS since the data is stored in the cloud and involves a third-party provider.
Fortunately, this is becoming less of a concern now since all major cloud providers now invest a lot of resources into securing their infrastructure and their cloud-based AI solutions. Most also offer services that are aligned with various data privacy regulations.
Generally, organizations can better control the security of their on-premises AI systems and decide what works best for them.Thus, they can install firewalls and antivirus software, set user access policies, and implement security patches to guard against attacks and breaches.However, if these controls are missing or weak, these solutions are highly vulnerable to security threats.
And missing or weak controls are often a result of a shortage 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 from adequately securing their on-prem AI systems, leaving them open to all kinds of cyber attacks.
Finally, many organizations fail to set and follow robust security standards for their AI solutions. Such misses can create multiple security 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 both on-prem and AIaaS. But in general, cloud providers do all they can to secure their infrastructure and protect their customers’ AI systems and data. This creates a security advantage for organizations adopting AIaaS, which adds to it's many other advantages, such as:
· Access to advanced infrastructure without the hassle of 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
We understand that not all companies can adopt cloud solutions due to their internal policies. For those, we are ready to provide on-premise containerized solutions.
However, we take the security of our AI products and cloud solutions 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 data transfers between web browsers and websites
· Two-factor authentication (2FA) to minimize the risks associated with password theft and compromise
· JSON Web Token (JWT) to secure RESTful APIs and ensure safe data transfers during two-party interactions
· HMAC authentication to authenticate API calls, ensure data integrity, and protect against man-in-the-middle (MitM) attacks
In addition, all BotX solutions are protected by multi-layered security consisting of:
· CDN firewall
· Server firewall
· 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.