The Role of AI And Cybersecurity
AI and cybersecurity go hand in hand. As companies continue to expand, they require more hardware and software. Combine that with emerging work from home and bring your own device type policies, and traditional methods of cybersecurity may no longer accomplish the job.
Growing companies have an ever-growing number of vulnerable attack points. Analyzing and protecting these points is no longer a job that can be accomplished on a human scale. This is where artificial intelligence can help. AI can do anything from pinpoint attempts to hack into your server, to identifying behavior that may leave data increasingly exposed.
What It Means to Have AI In the Fight
You may have advanced technology software that protects your company’s data, but that doesn’t necessarily make it AI. The main difference is this: AI becomes more intelligent with more data. It becomes more efficient, or smarter at the job.
Alternatively, many cybersecurity programs may simply employ data analytics. With data analytics, you are collecting data points to make the next logical conclusion, but there is no cognitive ability on the part of the program.
AI Improves Cybersecurity by Employing New Knowledge
AI and cybersecurity both improve when the technology can employ its new knowledge to protect company data. This is done through machine learning, neural networks, or deep learning.
It becomes an ideal tool for solving cybersecurity issues when tasks like threat detection become automated. A medium-sized company may have hundreds of devices and only a handful of skilled cybersecurity professionals on staff. There may be more data stored than any one person can analyze or protect.
When the scale of data and information passed around is beyond what any one person can protect, AI becomes a dependable option for asset protection. Once AI is properly trained, it becomes an extension of your cybersecurity team.
What AI and Cybersecurity Can Accomplish
Below are just a few of the tasks that a properly trained AI can handle in order to help with cybersecurity:
- Asset inventory – This includes getting a scope on the number of company devices and data volume surrounding a company.
- Threat risk – AI can establish patterns in hacker behavior and company exposure to determine the level of risk that your company is currently under.
- Improved Response – Without AI, it may take days, weeks, or months to even realize that data has been breached. AI can help with an immediate response.
The bottom line is that attacks are becoming more advanced as well. The best tools available to protect your data are also being used to coordinate the attack in the first place. The ability for AI to detect and help protect companies against evolving threats provides a distinct advantage in cybersecurity.
AI and cybersecurity will continue to grow together. An AI and human partnership in protecting company data will be a powerful combination that allows companies to concentrate on their core strengths for years to come. It’s the best way to detect threats, coordinate responses, and protect valuable company data in a world that contains ever-evolving threats.
Data Collection and AI
Data collection and AI are increasingly important tools for a variety of human tasks. Our use of artificial intelligence is only limited by our imagination, and we’re able to do more with it seemingly every day. Programs are being built that can beat the smartest humans at our favorite games, such as chess and go. Soon it will be driving our cars, cooking our meals, diagnosing our medical conditions, working in law enforcement, and more.
These AIs work on mass data collection, at a scale beyond what most humans comprehend. The definition of data collection is simply gathering and evaluating information from countless sources. But when AI is involved, the scale of the operation accelerates. That AI needs to be able to account for nearly any variable that might be thrown in its direction. When we’re talking about a task as monumental as driving a car, that’s a significant number of variables.
That data collection allows AI to find recurring patterns. From those patterns it can use machine learning algorithms to form its own predictive models in order to establish trends. This is how AI can be used to reduce the number of traffic accidents, or more accurately diagnose our medical conditions. It’s why there is potential to use AI in the law enforcement world to help get criminals off the streets.
But there are massive amounts of data that need to be collected in order to safely incorporate AI to complete these functions.
How Much Data is Needed for AI?
The reality is that there is no perfect answer to that question, and it is heavily reliant on the function that you are looking for the AI to complete. Many will build their early models with as little data as possible to keep it working, for simplicity’s sake. There are complex mathematical formulas designed to let data scientists and engineers know when it is time to stop and when you need to collect more data to make it work.
More data is not always the answer. Sometimes there’s no more data to be had, and the options are to either generate new data points based on what you currently have (data augmentation), or to create new data points using complex sampling techniques (data synthesis).
Data collection is almost always the best option for AI, especially if there’s more data to consider. It will give the model more accurate data points to rely on.
Data Collection and AI: Protecting What’s Collected
Data collection and AI are forever intertwined, and that comes with a new set of cybersecurity risks. Companies using data to build complex AI systems will need to be ever-vigilant at protecting that data from hackers and general data loss. This is especially true in cases where the data collected may include sensitive information.
Practical steps such as using a secure network, backing up data, and making sure your data is password protected will all work to safeguard against data loss and potential cybersecurity issues.
And in the event of a data loss, companies may be tempted to try free software and other popular means to attempt recovery. But that route can often result in more damage. An experienced data recovery company may be their best chance at regaining access to that critical information.
AI and Data Collection for Law Enforcement
AI and data collection for law enforcement go hand in hand. It’s easy to see how the vast collection of data on individuals across the nation can be used to help prevent crime and enhance society’s greater good. Criminals use the same tools as non-criminals: smartphones, laptops, social media platforms. They leave a trail that can easily be mined for law enforcement purposes.
But there is a fine line with this sort of data collection as well. Our law enforcement AI would only be as unbiased as the data it collects. It’s imperative to make sure that the data collected is accurate and fully unbiased.
Establishing Criminal Intent
What people post on social media amounts to circumstantial evidence. It may or may not be an expression of criminal intent. It may or may not point toward resolve to commit a crime. Not all data points will be available, and therefore you’re likely not receiving a complete picture of what happens.
To understand the shortcomings of AI and data collection for law enforcement, it helps to look at another field. Researchers are developing AI to write advertising copy. The copy that is so far being produced currently falls short of the capabilities of a human copywriter that understands how to play on human emotion. The same thing holds true in law enforcement.
Criminal investigations and law enforcement involve more than a simple collection of data points. Eyewitness accounts, 911 recordings, and police body cam footage factor into criminal investigations. The human element is critical here for AI to develop an accurate track record for predictive policing.
While AI and data collection will become increasingly important tools for law enforcement, a human connection will always be vital in this field.
Data Breaches From Police Departments
Law enforcement is not immune from data breaches. As predictive policing and investigation AI are more heavily implemented, police departments will find themselves housing data in larger volumes. This makes them a bigger target for data breaches.
In June of 2020, hundreds of thousands of potentially sensitive files from police departments across the US were leaked online. The leak included 10 years of data from over 200 departments, fusion centers, and other law enforcement training and support resources.
The Los Angeles Police Department was also recently hacked, exposing the confidential personal information of up to 20,000 individuals. Names, dates of birth, email addresses, and passwords were all made public.
Data Collection for Law Enforcement
As police departments collect massive volumes of data to use in their investigations, they will need to safeguard that data and protect the people involved. Data collection for law enforcement will become more prominent in the near future. AI and machine learning will depend on this data in order to enhance the investigatory process. But safeguards need to be taken in order to ensure safety – especially when people may not have willingly given their personal data over. Police departments can back this data up, and work to encrypt it. They can make sure that it is password protected, and only accessed from secure networks.
Any of these steps may work to improve the security of data.