CIO Influence
AIOps Guest Authors Security

The Impact of AI on Security Issues

The Impact of AI on Security Issues

AI has seen exponential growth during the past few years and it is completely redefining industries and changing the way we interact with technology. With the sudden rise of GPT technology, AI-led applications can now generate a wide variety of conversational responses across a variety of media like text, image, music, and video. This meteoric rise of AI has ignited a global frenzy of businesses and organizations across the globe embracing AI. However, with this adoption and development of AI technologies, there are serious security and privacy risks that need to be addressed.

Cybercrime Busters: Top 5 Cybersecurity Insights for Enterprise Storage

The Impact of Rapid AI Development on Data Security

The sudden rise of AI and its adoption has outpaced businesses in their ability to address security risks effectively. With the sudden emergence of GPT technology, businesses saw immense potential in the use of AI technologies and rushed to inculcate AI into their operations. However, during this process, a lot of businesses have failed to consider the security and privacy implications.

Detecting & Responding to Data Security Threats

The speed of AI’s evolution, especially in natural language processing and image recognition, has been staggering. Businesses have been quick to integrate AI into various aspects of their operations, from customer service chatbots to content generation. Still, they have not been equally swift in addressing the security concerns that come with these advancements.

Mitigating the data security threats arising with the use of AI necessitates a proactive response from organizations. There are two key strategies that are at the forefront of data security: Data Encryption and Immutability.

Data Encryption

The first method, data encryption, involves encrypting the data and then sharing this encrypted data with the AI model with a proxy re-encryption key. This ensures that even if the data is compromised, it remains undetectable and unreadable to the attackers.

Immutability

This approach involves keeping the data unencrypted but making it immutable. Immutability keeps malicious people from changing or deleting the data.

Decentralized Storage

Most of the attacks occur when data is at rest and if the data is split up into fragments and stored on multiple servers and providers then even if one server or provider is compromised the hacker would only have access to a portion of the data, not the full content. Data encryption and immutability provide an additional layer of security.

Mitigating The Risks of Data Security Threats

To mitigate data security threats, organizations can also have a duplicate data set in a secured facility which can be used for the AI models to provide a response in case of a disaster for the primary data source. This disaster recovery can be efficiently done by storing the data on a decentralized storage with validation from each provider that the data uploaded on the provider is the data that is served by it so that the AI is not compromised by a corrupted data set.

Latest AppSec Report Finds Software Vulnerabilities Decreased by 14% in 2 Years

Implications of Manipulated Data Sets

A manipulated data set changes the way the AI will respond to a query for anything related to the data. The input data drives the response and if the data is changed, then the response would change. So if someone wants to provide misinformation to an audience, they just need to change the input data with the misinformation.

Prioritizing & Implementing AI Data Security Measures

1.   Distributed Storage Network

One of the keys to achieving AI data security is the use of distributed storage networks. This ensures that only a portion of the data resides in a single server and in case of a security failure, the entire data set is not compromised. This means there is no single point of failure and security gets massively improved.

The added layer for data encryption and immutability on the distributed networks protects the data from being manipulated or deleted.

2.   Multi-Cloud Storage

Adopting hybrid and multi-cloud storage allows businesses and enterprises to avoid vendor lock-in and create an additional layer of security. However, it can be expensive to incorporate security, privacy, and disaster recovery through the use of multi-cloud. This platform allows easy application of AI security measures through its multi-cloud storage infrastructure. 

3.   Use of Blockchain Technology For AI Data Security

Blockchain provides trustless solutions with complete transparency and immutability. It enables validations to catch storage providers that do not store your data or lack performance. As for decentralized storage, it stores the allocation root on the chain, which is then used for challenges randomly generated by the blockchain to make sure the provider is able to pass them successfully. It also provides validation for a file downloaded by a user to make sure that the content is the same as the one uploaded by the content owner.

The rapid rise of AI has created numerous opportunities for businesses and individuals across the globe, however, there are significant security and privacy challenges that need to be addressed. These challenges can be addressed through the use of decentralized storage solutions, businesses can encrypt their data as well as make it immutable through the use of blockchain technology. In addition to this, the use of distributed and multi-cloud allows businesses to build an added layer of security and keep attackers at bay.

[To share your insights with us, please write to sghosh@martechseries.com]

Related posts

Guardsquare Names Technology Veteran Kimberly Wood as Chief Information Officer

Ordr Announces Integration with ServiceNow Service Graph Connector Program

PR Newswire

HCLSoftware Unleashes New DRYiCE Intelliops Features to Enable Business Excellence With AI at the Forefront

CIO Influence News Desk