AI attacks can be dangerous. Here are some tips for you to save your revenue from AI attacks

AI attacks are increasing in sophistication, which 80% of companies agree is making detection more difficult. As Kasada's report found, 85% of companies have had trouble stopping bad AI bots, as their existing solutions became ineffective a year after initial deployment. The most important finding of this research is that companies are still losing large amounts of revenue due to bad bots, despite their investment in dedicated anti-bot solutions. Those responsible for preventing bot attacks often report feeling overwhelmed at their inability to keep up with how fast they evolve. With attackers regularly adopting new tools or changing their methods of attack, only 31% were very confident in their solution's ability to detect new zero-day bots never seen before. In fact, 76% said they were either playing a game of cat and mouse with attackers or feel like bot mitigation has become an impossible balancing act.

Artificial intelligence could be powering unprecedented revenue growth until it does. That lesson is being learned the hard way in a growing number of companies where issues with systems are not caught and remedied before they affect revenue. The latest example is Unity Software, a platform for creating and operating interactive and real-time 3D (RT3D) content. Unity revealed that it missed top-line expectations and lowered its revenue guidance for the rest of the year due to a "self-inflicted wound" in AI.

It can happen to hundreds of companies. It's not just about Unity or Zillow or any other company that's making headlines; The potential for problems with models lurks in every industry, waiting to be uncovered. An increasing number of enterprises are making similar disclosures on their annual reports. According to a recent paper, 47 companies one in ten (9.4%) of the Fortune 500 cite AI and machine learning as risk factors in their most recent annual financial reports, up 20.5 percent year-over-year. This probably mitigates the risks given to the large number of companies leveraging AI in production.

Check the Possibilities Before Deploying AI

When it comes to deploying AI, it's a matter of when – if not, the models will face issues in production. Unlike the largely rule-based system of software development, successful results in machine learning are dependent not only on the system's health but also on the various complexities of the model and underlying data layers. Concept and feature drifts, training-production skews, cascading model failures, data pipeline issues, and outliers challenge even the most sophisticated machine learning teams that deploy models that perform flawlessly in training.

Implement ML Observability

Of course, knowing that there is a problem is only half the battle won; teams also need to figure out why. 84.3% of data scientists and ML engineers today cite the time it takes to detect and fix problems with their machine learning models, with one in four saying it takes them a week or more. Full-stack machine learning observability with ML performance tracing can help close this gap by helping teams automatically pinpoint the source of model performance problems. Leveraging a platform like Arize, teams can automatically bring out the groups where the performance impact or drift impact is greatest and adjust accordingly.

Right Investments in Right People

In its most recent annual financial report, Unity Software flagged the company's ability to compete for talent as a potential risk factor, noting that it has "engineers experienced in designing and developing cloud-based platform products". And "the competition is intense" for both "data scientists" with experience in machine learning and artificial intelligence.

ML teams Must be Close to the Businesses they Serve

Great things happen when the model maker and the product owner are in alignment; The opposite is also true. If businesses add centralized machine learning teams or AI centers of excellence to their tech organizations, one thing that shouldn't be lost in the process but doesn't often (more on this in the future) is close collaboration with internal customers. Technology can help. Leveraging ML observation capability, data scientists and ML engineers can link model metrics to business results – sharing results with product teams and business executives.