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Cybersecurity Measures to Prevent Data Poisoning Cybersecurity Measures to Prevent Data Poisoning
New and developing technologies like artificial intelligence (AI) and machine learning (ML) are vital in improving industries and daily life worldwide.... Cybersecurity Measures to Prevent Data Poisoning

New and developing technologies like artificial intelligence (AI) and machine learning (ML) are vital in improving industries and daily life worldwide. However, bad actors always look for ways to twist these emerging technologies into something more sinister, making data poisoning a serious issue that you should be prepared for.

What Is Data Poisoning?

Data poisoning happens when an AI or machine learning system generates false information due to feeding it bad data. Misinformation campaigns, bad actors, and fearmongers can deliberately compromise public-facing information to defame others or protect vested interests. Since training AI and ML models takes massive amounts of data, bad actors can manipulate them by peppering data sources with incorrect information. 

Data poisoning comes in many forms. Here are the three kinds of data poisoning AI developers should be aware of:

  1. Intentional misinformation: Defamation and slander long-standing issues. People can use the internet’s broad reach and immediacy to spread lies and false information to damage other’s reputations and put them in a bad light.
  2. Accidental poisoning: The internet is filled with data. While much is factual information, many pages still contain opinions and erroneous claims that AI platforms may find challenging to verify.
  3. Disinformation campaigns: Organized disinformation is still prevalent today as governments and organizations have something to gain from spreading fictitious narratives online and elsewhere. Online channels — specifically social media — are prime targets of disinformation campaigns meant to change people’s opinions.

Is Data Poisoning a Real Threat?

Aside from publishing erroneous information and proliferating deep fakes online, bad actors can also directly poison databases to manipulate the results of AI and ML systems. Data poisoning attacks have become problematic due to the extensive use of AI and machine learning in industries and ordinary users’ daily lives. 

In 2021, 82% of data breaches came from phishing attacks, stolen credentials, and human error. Data poisoning can exacerbate the cybercrime problem by compromising spam systems, allowing more spam emails to affect a wider population.

There are many ways data poisoning can threaten society. Here are some of them.

  • Finding errors and retraining compromised systems is a time-consuming and expensive process. OpenAI’s GPT-3 model costs around $4.6 million to train and develop.
  • Extensive data poisoning can render AI and ML models useless, as compromised systems can only generate inaccurate results.
  • Poisoned data can help spread disinformation and harmful codes ridden with malware and other malicious payloads.
  • Poisoned data stores can lead to significant losses in many industries. Some serious consequences of data poisoning include fines, data loss, system and performance crashes, and reputational damage.

Cybersecurity Tips to Protect Against Data Poisoning

Data poisoning is more accessible now than ever. Before, it took criminals considerable time and resources to facilitate data poisoning attacks. With the help of new technology, modern criminals can infiltrate sophisticated models quicker and inject incorrect information into databases or make backdoors that allow unfiltered access to once-secure systems.

IT and cybersecurity professionals must stay vigilant to catch attacks and stop inaccurate data from compromising expensive AI and machine learning models. Here are several strategies that can help stop data poisoning attacks:

1. Ensure Databases Are Free From Error

Controlling the data source is one viable defense against data poisoning. By securing massive databases before training, developers can ensure the information they feed into models is accurate and free from malicious content. Securing databases can be time-consuming initially, but it beats repairing compromised models after deployment.

2. Look for Anomalies During Training

Anomaly detection or monitoring data for suspicious patterns and content can save precious time and costly AI and ML model retraining. Data training can be laborious, but ensuring the data quality used in training systems can be a worthwhile investment for organizations.

3. Train Models to Identify Harmful Data

Although a machine learning system can be compromised by feeding it massive amounts of erroneous data, developers can also use data to combat data poisoning attacks. Data engineers can train models to identify potentially damaging information. This process augments the training data and helps models differentiate between facts and false claims.

4. Secure Data Handling and Storage

Cybersecurity teams must deploy stricter protocols when handling precious data. Access controls, encryption, and airtight data storage solutions make a difference in training a model. Compartmentalizing data sets can also keep assets secure. Keeping separate data sets for every asset will allow developers to contain the damage if bad actors compromise one data source.

5. Establish Strict Training Procedures

Machine learning developers must bolster their cybersecurity measures by restricting who has access to valuable data stores and training models. Keeping the training process secure and resilient to attacks will allow data engineers to train models using sanitized data sources. Verifying the integrity of data sources and strictly managing the training process can also help keep data sets secure.

Deploying Cybersecurity Measures in Training ML Models

The effects of data poisoning in training AI and ML models can be far-reaching. Organizations must take caution when handling big data for training purposes. Prioritizing cybersecurity measures and safety protocols can be time-consuming and costly, but they certainly pay off in the long run.

Zac Amos

Zac is the Features Editor at ReHack, where he covers data science, cybersecurity, and machine learning. Follow him on Twitter or LinkedIn for more of his work.

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