THE DOWNSIDES TO ARTIFICIAL INTELLIGENCE

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Security is of utmost importance in everything but for this article we will more focused on the security of organizations. It is in the best interest of every organization to take measures to secure artificial intelligence investments. This article will detail the side effects to artificial intelligence.

Yes, artificial intelligence is meant to make life easier but it will also open the door to security challenges of far reaching proportions. Security and privacy concerns are the major barriers to the full implementation of artificial intelligence. The security, performance and privacy of artificial intelligence models can easily be compromised by dubious actors.

Despite the level of damage this challenge can cause, the promised benefits of the implementation of AI has kept the interest of organizations. It seems the numerous benefits are slowly beginning to out-weigh the fear of security and privacy breaches.

This is probably the reason why the current machine learning and AI platform market is yet to come up with consistent and comprehensive tooling to defend organizations. What this means in the long run is, the technologies that will be produced in the coming years will not have any authentic defense against benign

and malicious attacks alike. In other words, organizations and consumers of this products are powerless against any form of security threats.

This thus upholds the need for organizations to put measures in place to counter any form of threat on their Artificial intelligence investments. It is important to note at this point that artificial intelligence not only threatens the privacy of users but it also threatens the performance of technologies dependent on AI.

Users of AI are exposed to three major risks (security, social and liability risks).

Security risks are on the up rise, threats with security simultaneously grow as more versions of AI are merged into enterprise operations. There might be a bug in an elevator that leads to an uncontrolled jam, for example.

Liability risks refer to the risks incurred as a result of the use of AI models on sensitive client data. This stems from the possibility of mistakes made as a result of the use of AI, possibly due to faulty programming. The implications of this risk are far reaching but take as an example the use of AI for votes collection and computation in an election, and as a result of faulty data collection no contestant could be announced the winner or worse the wrong contestant was announced winner.

Social risks on the other hand refers to the issue of simply irresponsible AI.

These risks can occur as a result of malicious inputs or query attacks

Why AI can easily be attacked by criminals

Malicious inputs can be seen as a form of opposing AI, malicious digital or physical inputs. Opposing AI can be socially engineered humans with AI-generated voice, which can be used for any type of crime – a new form of phishing. For example, AI synthetic voice and be used to impersonate anybody from the president of Nigeria to the woman selling fishes in the public market.

Query attacks allow criminals send queries to the artificial intelligence models of organizations in a bid to understand how these models work. Specifically, a black box query attack determines the uncommon, perturbated inputs to use for a desired output, such as financial gain or avoiding detection.

A white box query attack on the other hand regenerates a training data set that will produce a similar model which will invariably steal data from the organization.

How to secure your AI investment

It is important for users of AI to acknowledge the security and privacy threats that accompany the implementation of AI. In addition, they have to take measures

against security breaches as any form of AI breach will greatly jeopardize the brand and operations of an organization. Existing pillars of AI security include (Human focused and enterprise security controls) and the new security pillars (AI model security and AI data integrity).

AI model integrity encourages organizations to explore adversarial training for employees and reduce the attack surface through enterprise security controls. The use of blockchain for provenance and tracking of the AI model and the data used to train the model also falls under this pillar as a way for organizations to make AI more trustworthy.

AI data integrity focuses on data anomaly analytics, like distribution patterns and outliers, as well as data protection, like differential privacy or synthetic data, to combat threats to AI.

In order to secure applications and AI models within an organization, it is important for technical professionals to implement the following:

Conduct a threat assessment and apply strict access control and monitoring of training data to minimize the attack surface for AI applications during development and production.

Augment the standard controls used to secure the software development life cycle

(SDLC) by addressing four AI-specific aspects: threats during model development, detection of flaws in AI models, dependency on third-party pretrained models and exposed data pipelines.

Defend against data poisoning across all data pipelines by protecting and maintaining data repositories that are current, high-quality and inclusive of adversarial samples. An increasing number of open-source and commercial solutions can be used for improving robustness against data poisoning, adversarial inputs and model leakage attacks.

It’s hard to prove when an AI model was attacked unless the fraudster is caught red-handed and the organization performs forensics of the fraudster’s system thereafter. At the same time, enterprises aren’t going to simply stop using AI, so securing it is essential to operationalizing AI successfully in the enterprise. Retrofitting security into any system is more costly than building it in from the outset, so secure your AI today.

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