Why Data Security is Critical to Creating Effective AI Programs Why Data Security is Critical to Creating Effective AI Programs
Artificial intelligence (AI) programs have hogged headlines recently and the industry is set to grow exponentially over the next few years.... Why Data Security is Critical to Creating Effective AI Programs

Artificial intelligence (AI) programs have hogged headlines recently and the industry is set to grow exponentially over the next few years. As models such as the ones released by OpenAI grow and disrupt several business activities, data has become more important than ever.

Observers typically associate data security with large enterprises and their networks. However, data security is paramount to AI programs too. Several AI initiatives take ata integrity for granted, reasoning that security measures earlier in the analysis chain have accounted for any issues.

This approach fails to account for malicious attacks targeting AI initiatives. In no particular order, here are 3 reasons why data security is critical to building effective AI programs.

Models can Suffer “Poisoning”

AI model poisoning is a relatively new term that is set to enter mainstream jargon soon. Briefly, model poisoning refers to malicious actors injecting confusing data into AI learning sets. As a result, the AI misinterprets results, leading to serious consequences.

In AI’s previous iteration, gaffes such as an algorithm mistaking a muffin for a sloth were commonplace. While those algorithms were unsophisticated, a lot of those errors occurred due to injecting confusing data into learning sets, causing the AI to override manual annotations while learning.

Currently, algorithm intelligence is growing at a faster pace than ever, making such attacks even more devastating. While earlier AI iterations had few business applications, recent AI models are used by companies in everything from fraud detection to debugging code.

Model poisoning can also be used as a distraction when an attacker compromises their true target. For instance, an attacker can inject confusing data into a model’s learning set, prompting incorrect results that absorb a company’s resources while it fixes these errors, resulting in lesser coverage of other critical assets.

Aside from the embarrassment incorrect AI results generate, the financial losses stemming from a large-scale attack will cripple a business. Data security solutions deployed at every step of the AI analysis chain will mitigate these issues, protecting businesses at all times.

Data Privacy is Critical

The previous decade witnessed the rise of sophisticated data privacy laws and frameworks and increased consumer awareness of the importance of data security. As AI rises, consumers are likely to demand greater data privacy protection and ask for more justification when giving AI models their data.

Companies must therefore ensure they implement data security frameworks and communicate how they plan on using consumer data. Several AI projects have not executed this plan correctly right now. For instance, OpenAI’s models do not clearly communicate how the company plans on using the data its algorithms are fed, raising questions surrounding privacy and company IP.

As consumer awareness grows, companies cannot afford to ignore data privacy and transparency. The best way to ensure they meet these goals is to reduce the complexity of privacy policy documents. Ironically, companies can use AI to generate simplified versions of privacy policy documents, ensuring their users are always aware of the consequences of inputting data into algorithms.

Data security also ensures sensitive data will not fall into the wrong hands. As AI grows, the amount of data companies collect and generate is set to increase exponentially. Data security is therefore critical to company success.

Combating Insider Threats

Insider threats have always presented a challenge for cybersecurity programs. As AI rises, it will displace humans in critical roles, leading to increased resentment. As a result, malicious insider threats are bound to increase over the next few years.

Combating these threats is challenging because most cybersecurity tools focus outward. They secure networks from external attacks. Insiders bypass those walls and cause cybersecurity solutions headaches as a result. Companies must adopt agile security methods such as Zero Trust policies and time-based access to eliminate the impact of these threats.

Another way of combating this rise is to plan a roadmap to AI adoption. Some companies have been springing AI as a surprise to their employees who live in fear of being made redundant. Instead, companies must be transparent in their progress toward AI adoption and communicate workforce plans.

Offering employees the chance to upskill or move into complementary roles is crucial to boosting efficiency. It also lets employees know that AI isn’t here to take their jobs. Instead, it helps them execute faster and dive deeper into value-added work. 

Company executives must take the lead and make these steps a part of company culture. By doing this, companies can eliminate the looming insider threat and secure their data better.

AI Needs Preparation and Security is the Key

Adopting AI sounds great on paper, and the advantages are huge. However, companies must prepare their AI adoption paths to avoid risking major data security breaches. Ensuring security at all points in the analysis chain, offering transparency to users, and ensuring employees are onboard are critical pillars in this path.

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