Connect with us

TECHNOLOGY

How Artificial Intelligence Can Assist Whistleblowers

Published

on

How Artificial Intelligence Can Assist Whistleblowers


Artificial intelligence (AI) is helping whistleblowers expose the truth in several ways.

Organizations and businesses must give whistleblowers the freedom to expose the unethical ways in which AI and other technologies may be used by them. Doing this ensures that the true ethics of AI are being upheld.

Although mega-corporations and governments always harp about “encouraging” whistleblowers to come forward and “speak out” about technologies such as AI and IoT being used for morally wrong motives, there is a reason why that doesn’t happen too often. For starters, one can simply look at Edward Snowden, arguably the most renowned whistleblower and one of the true icons of the modern age, and his relentlessly perilous life. In 2013, Snowden, who was granted permanent residency in Russia in October 2020, showed the fortitude to expose powerful bodies such as the NSA and CIA in the US. Such whistleblowers have inspired several others to emulate them. More recently, another whistleblower Frances Haugen came forward to shed light on the unethical ways in which Facebook abuses user data.

As the world moves forward, businesses and governments must uphold the ethics of AI and leverage the technology to help whistleblowers instead of silencing them or hunting them down like prey.

Whistleblower Reporting with AI Chatbots

Chatbots can autonomously submit the reports created by a whistleblower to a designated department while keeping their identity hidden. Additionally, this AI-based application can interactively assist whistleblowers with getting the process of manually submitting reports and following up with such departments right. Using NLP, chatbots can also formulate responses to reports to prepare whistleblowers for potential backlash and questions. It wouldn’t be wrong to say that chatbots can act as virtual agents for whistleblowers by helping them achieve their objective of either putting out their reports in the electronic and social media or reporting it to the top decision-makers in companies and public bodies.

AI_helping_whistleblowers.png

As there are no humans involved in the report submission process, chatbots allow whistleblowers to anonymously speak out without hesitancy.

Mechanical Whistleblowing with Robotics

Alternatively, robotic whistleblowers can be employed for identifying people in an organization or public agency who carry out unethical practices in day-to-day work. This monitoring must be done at all levels—for the board of directors, managers, operational employees and others—in an organization. Some robots can also be configured to collect data from various employees to compile information and create their own whistleblowing reports. Robots can also analyze every operation in an organization and verify whether they’re fundamentally ethical or not. Robotics-based whistleblowing takes the weight of “speaking out” from the shoulders of human whistleblowers to keep them safe from strict action from their employers or public agencies.

One of the main benefits of using robots is that whistleblowers will be kept away from various legal entanglements that typically accompany a whistleblower’s reveal. 

Conclusion

Although AI offers some solutions to help people who need to bring ugly tech-related truths out in the open, businesses and governments must make the task of whistleblowing easy for any employee. This will ensure that the ethics of AI—transparency, equality, and fairness, among others—are respected and upheld by them.



Source link

TECHNOLOGY

How Deep Learning Has Proved to Be Useful for Cyber Security

Published

on

How Deep Learning Has Proved to Be Useful for Cyber Security


The threat of cyber attacks has recently increased dramatically and traditional measures now appear to be insufficiently competent.

Because of this, deep learning in cyber security is rapidly gaining ground and may hold the key to solving all your cybersecurity issues.

With the advent of technology, there is also an increase in threats to data security and the need to protect an organization’s operations using cybersecurity tools. However, companies are struggling due to most cybersecurity tools being dependent. They rely on signatures or evidence of compromise for the threat detection capabilities of the technologies they use to safeguard their business. Because they are only useful for identifying risks they are already aware of, these technologies are useless against unknown attacks. Here is where deep learning in cyber security can alter the course of events. Deep learning, a branch of machine learning, is excellent at using data analysis to address issues. By subjecting the deep neural network to a vast quantity of data, which no other machine learning in the world can handle, digest, and crunch, we are mimicking the brain and how we operate.

USES_OF_DEEP_LEARNING_IN_CYBER_SECURITY.png

The cyber security industry is facing numerous challenges and deep learning technology might just be its salvation.

Behavior Analysis

An essential deep learning-based security strategy for any firm is tracking and examining user activities and habits. Since it goes beyond security mechanisms and sometimes doesn’t trigger any signals or alerts, it is substantially harder to spot than conventional malevolent behavior against networks. For instance, insider attacks happen when employees utilize their legitimate access for nefarious purposes rather than breaking into the system from the outside, making many cyber protection systems ineffective in the face of such attacks.

 

One effective defense against these attacks is User and Entity Behavior Analytics (UEBA). After a period of adjustment, it can learn the typical patterns of employee behavior and identify suspicious activity that may be an insider attack, such as accessing the system at odd hours, and then raise alarms.

Detection of Intrusion

Intrusion Detection and Prevention Systems (IDS/IPS) are capable of identifying suspicious network activity, blocking hackers from gaining access, and notifying the user about the same. They are generally characterized by well-known signatures and common attack formats. This is helpful in defending against risks like data leaks.
Previously, ML algorithms handled this operation. However, the system generated several false positives as a result of these algorithms, which made the work of security teams laborious and added to their already excessive exhaustion. By more accurately analyzing the traffic, lowering the number of erroneous alerts, and assisting security teams in differentiating between malicious and lawful network activity, deep learning, convolutional neural networks and recurrent neural networks (RNNs) can be used to develop smarter ID/IP systems.

Dealing with Malware

A signature-based detection technique is used by conventional malware solutions like typical firewalls to find malware. The business maintains a database of known risks, which is regularly updated to include brand-new dangers that have recently emerged. Although this method is effective against basic threats, it fails to counter more sophisticated threats. Deep learning algorithms can identify more complicated threats since they are not dependent on the memory of well-known signatures and typical attack techniques. Instead, they become familiar with the system and can see odd behavior that can be a sign of malware or malicious activity.

Email Monitoring

To stop any form of cybercrime, it is essential to monitor the employees’ official email accounts. For instance, phishing attacks are frequently carried out by sending emails to employees and requesting sensitive information from them. Deep learning and cybersecurity software can be used to prevent these kinds of attacks. Using natural language processing, emails may be checked for any questionable activity. Automation is essential for defending against the enormous amount of risks that businesses must deal with, but ordinary machine learning is too constrained and still needs a lot of tweaking and human involvement to produce the desired outcomes. Deep learning in cyber security goes above and beyond to keep improving and learning over time so that it can foresee hazards and stop them before they materialize.



Source link

Continue Reading

DON'T MISS ANY IMPORTANT NEWS!
Subscribe To our Newsletter
We promise not to spam you. Unsubscribe at any time.
Invalid email address

Trending

en_USEnglish