Even assuming a HiBob data breach itself stays improbable, HR still needs to lock down vulnerabilities on their end around device policies, access controls, security training, etc. Partnering with a vendor offering state-of-the-art encryption and access controls gets you halfway there. Building an organizational culture focused on responsible data privacy completes the equation.

So in this guide, we will share the latest safeguards companies need to put in place so HR pros are equipped with tactical ways to cost-effectively shore up defenses across email, spreadsheets, messaging apps, and cloud storage. Because even if your core HR database achieves fortress-like security, sensitive employee info persists in other scattered places that still warrant attention.

Understanding The Types of Employee Data at Risk

To start securing sensitive employee data better, we need clarity on what types of information are most at risk in today’s digital workplace. There’s a lot we need to be protecting better, including:

• Personnel Records: This includes compensation information, payroll details, social security numbers, background check results, offering letters with salary data – basically all the private stuff many employees don’t want getting out.
  
• Medical History: Any health data HR has like medical leave documentation, disability status, workers compensation files, drug test results, etc. falls into this bucket. You can never be too careful given how damaging exposure could be for employees.
  
• Internal Incident Reports: Investigation records related to employee misconduct charges, terminations, harassment allegations or grievances/complaints filed should be closely guarded.
  
• Email and IM Records: While not as intensely personal as health history, archived email and chat tools like Slack contain lots of potentially sensitive data such as system credentials, source code, and upcoming product launches. This data needs to be compartmentalized and secured.

Bottom line, nearly all employee data that grants visibility into someone’s private life or proprietary company information needs to be treated as sensitive and confidential from a security perspective.

Examining the Cyber Threat Landscape

With everyone now accessing company data from personal devices and cloud apps, visibility has gotten very messy for IT and HR admins. As such, lurking criminals and disgruntled staff have more routes than ever to try exploiting this info. Here are some worrisome scenarios you need to get ahead of:

• Phishing Schemes – Watch out for emails aimed at tricking people to click dangerous links or type their passwords into fake login pages. One wrong click can expose contacts to spear-phishing attacks.
  
• Ransomware Attacks – Malicious software that encrypts company systems until payments are made. We’ve seen government HR and payroll departments taken hostage this way.
  
• Network Breaches – Skilled hackers penetrating defenses and grabbing employee data when our vigilance slips. No industry has proven immune if they have valuable people data.
  
• Insider Leaks – Remember that while external threats loom, some disgruntled employees may have a motive to leak sensitive info in revenge. Yet, well-meaning employees accidentally leaking data through improper data handling is actually a more common scenario than intentional theft. Stricter protocols and training helps here.
  
• Cloud Data Leakage – When we don’t configure tools like Slack or Office 365 properly on the security side, all it takes is one person syncing a compromised mobile device to put data in jeopardy.

7 Ways to Lock Down Employee Data

So what concrete steps can HR leaders take today to lock down employee data? Here are seven top practical recommendations:

Centralize Critical Data Storage

Maintain as few databases/repositories as possible containing personal employee info like health records or SSNs. When this data is fragmented across multiple tools or shared drives, it becomes nearly impossible to defend.

Create a single, hardened system of record.

Apply Strict Access Controls

Use strong role-based access permissions across all devices and accounts enabling employee data access. Require manager approval for expanded access. No weak passwords or unused legacy accounts allowed! Automatic session timeouts after periods of inactivity are wise as well.

Mandate Multi-Factor Authentication

Enforce strong MFA using biometrics, hardware tokens, or authenticator apps for any internal system or tool touching sensitive employee data. This includes cloud apps like Gmail, Slack, and payroll portals. MFA adds critical secondary protection if a password does get cracked or phished.

Mask Sensitive Data Fields

When displaying PII like SSNs is absolutely necessary, mask partial characters to avoid exposing full numbers without need. If all 9 digits aren’t required for a business process, don’t show them. This limits damage if systems are breached.

Encrypt Network Traffic

Mandate encryption for any employee data transmitted over office Wi-Fi, stored on endpoint hard drives, or backed up to the cloud. While this introduces some access latency, encryption renders stolen data entirely useless to cybercriminals even when compromised. It’s a must-have.

Conduct Ongoing Security Training

Require all employees complete modern cybersecurity and data privacy courses when onboarded and during annual refreshers. Doing this pays huge dividends improving mindfulness around risks. Cyber skills are essential for every modern employee!

Designate Responsible Security Leadership

Appoint an internal Chief Information Security Officer accountable for data protection, even if they only lead a small team initially. This executive-level role has become essential with the convergence of technology, workforce management, and business continuity happening in every industry. Security can no longer be an afterthought.

Final Word

The rise of AI and automation will only amplify technology risks around sensitive employee data loss and privacy threats. However, with deliberate strategy grounded in security best practices, HR leaders absolutely can foster workplace innovation while earning staff trust that their personal data remains protected.

Companies that invest equally in both people and technology will gain long-term advantage.

The post The Risks Threatening Employee Data in an AI-Driven World appeared first on AiThority.

Kubernetes (K8s) clusters are being targeted by cybercriminals. This is rather unsurprising but something that deserves ample attention. Backdoors and malicious software have been employed to attack the cloud systems of various small to midsize businesses and many Fortune 500 companies. Reportedly, the Kubernetes clusters of hundreds of open-source projects, organizations, and individuals were breached as they were reportedly openly accessible, making them easy targets.

This problem is only going to worsen if Kubernetes users fail to respond with a sense of urgency. It is high time to beef up cyber defenses for K8s clusters and workloads, especially for those who are new to embracing Kubernetes or containerized workloads in general.

The AI and Kubernetes Connection

Artificial intelligence and Kubernetes security are related because of several factors, but the key connection is the growing adoption of containerization in software development. AI developers package AI models and their dependencies to make them portable and reproducible. Also, they containerize AI workloads that involve complex libraries. Containerization makes resource management more efficient while also supporting multi-tenancy and collaboration. It also supports dynamic configuration and makes it easier to manage AI applications.

One of the popular solutions used in handling containerization is Kubernetes. It is an open-source platform designed to enable efficient and secure container management. However, just like most other tools, it is only as good as how users use it. If security functions are not properly configured and best practices are not observed, Kubernetes cannot guarantee the security of workloads.

For AI to take full advantage of containerization with Kubernetes, paying attention to K8s security is also important. AI development certainly cannot only focus on efficiency. With the growing volume, aggressiveness, and sophistication of attacks on various IT assets including AI, it is crucial to invest time and effort in effective security.

Addressing Misconfiguration

There is no doubt that misconfigurations are a serious threat against containerized workloads, and the recent news on K8s attacks on hundreds of organizations is just one proof of this reality. As reported, The cloud system attacks mentioned above have been successful because of two misconfigurations. One is about enabling anonymous access with privileges. The other is a configuration mistake that exposed K8s clusters to the internet.

Some tech pounds are saying that many of the default Kubernetes settings are not optimized for security. As such, administrators are advised to make sure that their K8s are thoughtfully configured to ensure that usability and efficiency settings do not lead to security weaknesses. It is also advisable to make good use of the K8s Secrets feature and not settle with plain text configuration files when working with SSH keys, tokens, and other sensitive information.

AI development is a complex process that can be compared to an assembly line with innumerable moving parts. Containerization with Kubernetes can help reduce the complexity, but the problem of misconfiguration is hard to escape. K8s security mindfulness includes the need to be on the lookout for possible misconfigurations.

Ensuring Data Privacy And Compliance

Regulators are setting their sights on AI development. There is a silent consensus among developers and consumers that AI regulation is needed, especially when it comes to protecting the data used to train AI models. With AI developments utilizing cloud and containerization, it is a given that Kubernetes will be on the laundry list of items regulators will examine.

There are several ways to ensure K8s data privacy, starting with the regulation of communications between pods within a cluster down to audit logging and the regular scanning of the Kubernetes environment for compliance issues. It is advisable to implement role-based access controls, encryption for both data at rest and in transit, as well as data minimization. Additionally, it is crucial to implement a well-thought-out secrets management system.

Securing Multi-tenant Environments

AI development is usually a collaborative endeavor. Multiple teams work together to produce intelligent systems for various applications. These teams usually work on different AI models or tests at the same time, so they need a system that supports multi-tenancy. This is something Kubernetes provides with a great deal of efficiency.

Multi-tenancy makes it possible for different teams or projects to work on the same cluster concurrently. To avoid access confusion, the concurrent users are isolated. Namespace segregation and storage isolation are also enforced along with resource quotas and limits. Additionally, role-based access control is implemented to ensure strict boundaries between different projects and also to block unauthorized resource access.

A secure multi-tenant environment is an important component of K8s security, which is important for AI development that harnesses containerization. It is essential to have well-defined network and pod security policies to prevent unauthorized or malicious access. It is also crucial to have secure ingress controllers for the management of external access to services within clusters.

Managing Vulnerabilities Efficiently

Artificial intelligence development entails the use of numerous dependencies and libraries, which are then encapsulated in container images. One of the best practices in Kubernetes security is the regular scanning of vulnerabilities as well as the continuous tracking and updating of these container images to spot potential risks and mitigate them before they advance into more serious concerns.

This vulnerability management capability is highly important for AI development to protect sensitive data and ensure model integrity. At the same time, it is useful in guiding regulatory compliance and building trust and reputation. The management of security vulnerabilities ensures that the resulting AI system is not only secure but also compliant with all applicable regulations.

Monitoring The Health Of Critical Tasks

One Kubernetes function that is partly related to security is the continuous monitoring of the health of AI applications. This function enables the tracking of different performance metrics, resource utilization, and possible issues. This helps in easily spotting problems in the AI system that is being developed. Also, it can provide insights into the root causes of problems affecting critical tasks.

In connection with the monitoring function, Kubernetes also has an orchestration feature, which enables the efficient distribution of AI workloads across the cluster. This is not necessarily a defensive function, but it helps in anticipating and preparing for cyber attacks to minimize downtime.

In summary, K8s security is important in AI development because it offers a handful of benefits including the reduction of the prevalence of misconfigurations. It is also helpful when it comes to data privacy and regulatory compliance. Additionally, it provides secure multi-tenancy for the convenience of various teams working on clusters at the same time. It also helps in vulnerability management and the monitoring of the health of AI applications.

[To share your insights with us, please write to sghosh@martechseries.com]

The post The Important Role of Kubernetes Security in AI Development appeared first on AiThority.

There’s no doubt AI is already becoming commonplace, even in the healthcare industry. Just recently, two tech giants announced new AI solutions for health and medical applications. Google presented the new functions of its Vertex AI Search tool designed for the healthcare and life sciences field. Microsoft, on the other hand, released some details about the healthcare function of its Fabric analytics solution.

A multitude of AI medical or healthcare products have already been deployed over the past years, ranging from patient monitoring wearables and implants to diagnostic imaging, digital pathology, and genomic sequencing solutions. These products have brought about numerous benefits in terms of healthcare facility operations and patient care. However, they have also resulted in the emergence of new vulnerabilities and risks, which in turn attracted the attention of regulators.

The medical and healthcare sector is already highly regulated, and it seems inevitable for more regulations to be imposed. However, there are currently no specific laws on AI use in medicine and health services, at least when it comes to major markets like the United States and Europe. There are no laws that target how AI is utilized like requiring a human doctor’s verification when making AI-aided diagnoses and regulating the dispensation of AI-powered automated medical services.

The regulations in force now are mostly about medical cybersecurity and data privacy. These are not enough, but even with these legal impositions, there is some resistance to having them enforced especially among businesses.

World’s First AI Healthcare Radio Station Now Streaming Through Spotify

Here’s a look at some of these regulations along with arguments on why they are important and the improvements needed.

Making sure that devices work as intended

AI-infused medical devices are viewed as advanced and perform unprecedented functions. Patients are either excited to try them or they hesitate to become the pioneering users because of the device performance uncertainties. Fortunately, regulations exist to ascertain that AI healthcare products are safe, effective, and free from cyber risks before they can be made available to the public.

There are a few medical cybersecurity regulations that help allay concerns over device safety, effectiveness, and cybersecurity. They are not specifically aimed at the use of AI, but they include provisions that can compel device manufacturers to ascertain that their AI systems behave in line with reasonable expectations.

In the United States, the Food and Drug Administration (FDA) has issued guidelines covering different stages of the lifecycle of products. There are pre-market requirements, particularly those outlined in FDA 21 CFR parts 807 and 814, that ascertain that products have been designed and manufactured within safety and effectiveness regulations.

 There are also post-market rules (21 CFR Part 803) that compel manufacturers to monitor their products for possible defects and other issues that may lead to user injury and other unwanted outcomes. Additionally, the FDA has regulations applicable across different product lifecycle stages to ensure product quality (21 CFR part 820) and to inform consumers about the cybersecurity of their products (21 CFR part 820).

Meanwhile, the European Union has the Medical Device Regulation (MDR)  2017/745, which replaced the EU Medical Device Directive (MDD). Just like the FDA regulations, this law requires medical devices to be safe, effective, and reliable. It sets mechanisms to ascertain that defects and other problems are reported and addressed in a timely manner. It also includes post-market evaluation and continuous improvement provisions. It requires device makers to compile readily available information about their products and make sure that consumers are made adequately aware of what they need to know about the products, especially when it comes to safety and effectiveness issues.

Again, these regulations do not specifically target the integration of artificial intelligence into medical products and services. However, they establish a way to force manufacturers to maintain acceptable standards of quality in designing and producing their devices. They also empower patients or consumers to have a role in the safety and effectiveness of the products available in the market.

AI Healthcare Startup AKASA Hires Phil Walsh as Chief Marketing Officer

Ensuring patient privacy and data security

Machine learning is all about accumulating data and using this data to continuously improve a system. In the process, this creates the problem of possibly exposing patient data. Without mandated guardrails in place, AI healthcare systems like AI bots used to interact with patients may reveal patient data to threat actors. They may expose information that should be kept confidential.

Fortunately, there are already existing laws that can be implored to prevent any system from unnecessarily revealing information to non-parties. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) and the European Union’s General Data Protection Regulation (GDPR), for example, have provisions that can be used to prevent AI systems from violating patient privacy and disclosing their private data to unauthorized entities. 

Similar to what happened with ChatGPT, it is not a remote possibility for AI bots used in healthcare to face privacy violation suits. Generative AI, the technology powering ChatGPT, will be used in creating patient-interfacing bots. As such, it is crucial to implement ways to keep AI systems from oversharing information, and regulations are there to make this happen.

Informing patients about product reliability, safety, and security

Product labels that indicate not only the uses, usage, safety, and cybersecurity of medical devices are a welcome addition to existing regulations. They help consumers find the best products for them by obliging product makers to provide correct information on their labels, especially when it comes to the effectiveness and safety aspects.

There have been attempts by the US FDA to enforce medical device cybersecurity labeling but this did not materialize, and the agency settled with a voluntary labeling system. It is not ideal but it is better than nothing. The EU has some provisions on labeling, but these do not cover safety and cybersecurity concerns.

In summary

There is a need for clear and specific regulations on using artificial intelligence in medical devices and healthcare in general. While there are existing laws and regulations that may be used to address worries over the use of AI in medicine and healthcare, they are insufficient and not explicit enough to reassure patients and consumers.

Regulations help address product reliability, effectiveness, and cybersecurity. They also facilitate patient data protection. Additionally, they can make it imperative for device manufacturers to aid customer choice through useful accurate information on product labels. FDA regulations, the EU MDR, the Association for the Advancement of Medical Instrumentation’s (AAMI) Technical Information Report 57, PATCH ACT, the International Medical Device Regulators Forum (IMDRF), and the Medical Device Coordination Group’s (MDCG) 2019 series of guidance documents need to be updated to reflect the growing prominence of AI in the healthcare field.

[To share your insights with us, please write to sghosh@martechseries.com]‌

The post The Role of Medical Cybersecurity Regulations in Protecting Patients with the Rise of AI-Driven Healthcare appeared first on AiThority.

Gartner recently released their Market Guide for Extended Detection and Response report. The report’s Market Description section states that “XDR can improve Security Operations staff productivity by converting a large stream of alerts into a condensed number of incidents that can be manually investigated efficiently,” and by “reducing training and skills needed to complete operational tasks by providing a common management and workflow experience across security products.”

Of the ten XDR vendors listed, only one offers an “Open XDR” technology.

But, what exactly is Open XDR?

The rise of Open XDR

One of the ways to keep up with the emergence of more sophisticated and aggressive threats is the integration of disjointed security solutions.

For decades, organizations have been using security tools and services from various vendors, which makes it difficult to have a comprehensive view of threats to spot and respond to them in a more agile manner.

This seamless consolidation of security solutions is achievable with open extended detection and response or Open XDR. Also known as open cross-platform detection and response, this cybersecurity technology is designed to integrate various security tools that used to be non-integrable or hard to bring together. It allows organizations to have a unified view of their cybersecurity situation and facilitates more efficient threat discovery, investigation, and response.

Open XDR is different from traditional XDR because it is not vendor-specific. It can pool security data from all security tools and conduct advanced analysis to ensure maximum security visibility and reduce complexity. It supports third-party integration, not just the integration of different tools from the same vendor or various tools that are already integrable.

Open XDR ensures rapid threat detection, optimum threat visibility, reduced false positives, and enhanced incident response under a scalable and cost-efficient platform that also supports continuous improvement. These benefits are possible through extensive, vendor-agnostic security tools integration.

Open XDR with Stellar Cyber

Stellar Cyber is the sole Open XDR vendor in Gartner’s report. They offer a unique approach to Open XDR implementation. Recognizing the pros and cons of the “Build/Acquire Everything” and “Integrate with Everything” models, Stellar developed a hybrid approach that takes into account the benefits and drawbacks of these opposing paradigms.

For those unfamiliar with these integration models, “Build/Acquire Everything” is about providing a consistent or predictable user experience by piecing together security solutions from the same vendor or collaborating vendors. Meanwhile, “Integrate with Everything” allows organizations to come up with combinations of security tools with almost no limitations.

The former may sound restrictive, but many organizations prefer it because they can readily use the resulting open XDR solution; they no longer have to go through the process of assembling different tools. The latter offers maximum flexibility, but it is not the best option for those who do not have enough experience and expertise in cybersecurity products.

Stellar Cyber acknowledges this major dilemma, so it offers a compromise between the two approaches. In particular, it provides an Open XDR platform that already has built-in network detection and response (NDR), security information and event management (SIEM), threat intelligence platform (TIP), and AI-powered enhanced detection and response functions. These capabilities are then integrated with other security solutions such as endpoint detection and response (EDR), intrusion detection system (IDS), and user entity behavior analytics (UEBA).

Stellar Cyber has an API and an AI engine that makes it significantly easier to integrate security tools and achieve the most comprehensive security visibility. The API supports seamless integration while the AI engine automatically correlates incidents and processes security alerts to prioritize the most urgent notifications and considerably reduce false positives, which are quite prevalent.

Open XDR with ‘Universal EDR’

Stellar introduced the idea of universal EDR, which is essentially an existing EDR solution that becomes Open XDR by integrating with Stellar’s Open XDR platform. Virtually any EDR product can become part of Stellar’s Open XDR, which supports more than 400 integrations out of the box. Even better, Stellar says that their Open XDR platform not only integrates with EDR systems but also makes them better.

In particular, Stellar’s Open XDR platform can improve the alert fidelity of the EDR being integrated. This is done through a system called “Alert Pathways,” which undertakes robust data normalization and enrichment, noise reduction, automatic correlation, and contextualization.

Alert Pathways has three main techniques, namely passthrough enrichment, deduplication, and machine learning event-based contextualization and correlation.

• Passthrough enrichment entails the normalization of data from the EDR system and the addition of complementing and supplementing data from threat intelligence, the MITRE ATT&CK framework, and other cybersecurity data sources to boost alert fidelity.
• Deduplication is essentially the removal of redundant and unnecessary information to reduce the amount of the data that requires processing and ensure more efficient responses.
• Lastly, the machine learning event-based technique employs different machine learning models to contextualize and correlate security alerts. This process results in better accuracy and timely responses.

Recommended Technology News: Hong Kong to Attract Thousands of AI & Web3 Companies as CoinW Prepares for Hong Kong Branch Launch

The post Unlocking Game-Changing Cybersecurity With Open XDR appeared first on AiThority.

If you’re developing an application or an e-commerce website, you know that each process follows a set of rules.

For example, when a customer places an order, the site will calculate the cost, discounts, shipping, and taxes. Then, it has to update the user’s account and notify them that their order is processing or being shipped.

This is known as business logic.

Cybercriminals who are familiar with the exact processes of your company can exploit them to obtain sensitive data of your company or make changes in algorithms.

Unlike traditional attacks, such as malware, DDoS, or phishing threats, business logic attacks exploit insecure APIs and non-technical flaws within applications.

BLA is a cyber attack that’s challenging to detect with traditional security tools. It can bypass the API security you have or the firewall that you set up to protect your application.

How?

When a bad actor compromises an application with BLA, security tools may not register it at all. They’ll perceive it as if the app is undergoing regular business logic processes.

Could Artificial Intelligence (AI) change that?

How do security tools that rely on AI discover and stop business logic attacks before they disrupt your company’s processes?

Let’s find out.

Identifying Anomalies Within the Application

Unlike well-known types of malware or other exploits that hackers use to compromise networks and apps, business logic attacks don’t follow a specific pattern cyber tools could detect to mitigate threats. The attack surface of applications is continually shifting as well.

Tracking all changes manually is both unsustainable and time-consuming.

This is where AI makes a difference. It can gather a large amount of data about behavior within the app and analyze its findings 24/7.

Since it continually gathers data, it can alert security teams as soon as it detects something that stands out — an anomaly.

For example, it can rely on the User and Entity Behavior Analytics that monitor how the users usually use the app. AI is used here to form user profiles and learn how they normally use the system.

After tracing the data about the business logic and users after some time, AI can spot anomalies. Those could be tweaked authorizations or changes in when and how (by whom) the purchase is processed within the software.

Enforcing Rule-Based Principles

In addition to collecting and analyzing data to provide the teams with alerts, AI can also help enforce rules that safeguard your app when it’s undergoing a suspected business logic attack.

Since every organization operates based on different business logic, every company can set its own custom rules to define what is acceptable and not within the system.

Some of the criteria they can consider are compliance, overall top security practices, or protective policies the business already has.

AI can be used to ensure that these rules are applied across the entire software environment. It decides whether to t********** based on the insights or send out an alert.

Sometimes, this means that the attempt to access a certain account will be blocked right away.

At other times, security teams will receive an alert that they need to investigate to uncover and mitigate advanced threats.

Protecting an App With Advanced Application Security

The cybersecurity solution that you set to prevent attacks on your application or website should be capable of not only detecting bot attacks but also spotting broken authorization, and business logic attacks.

The best way to fight business logic attacks is with comprehensive security that takes into account your entire attack surface. And then uses AI to analyze security data as well as to block the threats and suspicious behavior right away.

The advanced application security solution you choose to safeguard your apps and websites should be capable of:

• Taking into account the ever-shifting attack surface
• Not affecting the regular conduct of your business
• Helping you manage a large number of alerts

Responding to Potential BLA Attacks in Real Time

Together with continual data gathering and analysis of user behavior, AI can also help you respond to threats (detected anomalies) in real time.

This is integral for application security because the longer you wait to discover cyber-criminal activity within your system, the more damage they do to the finances and reputation of a business.

The average cost of a data breach for U.S. companies that have experienced it in 2023 was 9.48 million U.S. dollars. Unfortunately, many never recover from that.

Whether we discuss ransomware or insider threats, hacking activity is a time-sensitive operation. It requires an immediate response — especially when it can endanger assets such as personal data.

Can AI Fight Business Logic Attacks?

AI can be used to detect behaviors that indicate a business logic attack is taking place within your app or website. Every company has a different set of rules and processes. AI can keep track of them and respond to anomalies on time.

Business logic attacks are possible when a threat actor knows the software inside and out. For instance, they may know the exact time when purchasing orders are processed by an e-commerce company.

These attacks affect not only your inner systems but can also put customers who use your service at risk. Cyber attacks can lead not only to financial losses but also affect the public’s opinion of your company.

Most businesses focus on security to prevent traditional and known threats. They could lead to unauthorized access or data breaches by exploiting technical weaknesses within the app or the web.

Advanced application security solutions use AI to detect not only a wide range of hacking exploits but also often overlooked business logic attacks.

The post Can Artificial Intelligence Detect Business Logic Attacks Early? appeared first on AiThority.

Fraudsters are getting sneakier by the minute, leaving both companies and everyday people feeling under treat. From massive data breaches to growing cases of identity theft, it seems we’re all at risk of being the next target. And unfortunately, the numbers paint a grim picture – it’s estimated that between 2023 and 2027, online payment fraud alone could cost over $343 billion to businesses worldwide.

With these staggering figures, it’s clear the traditional tools for fighting fraud are no longer cutting it. Rigid rules and manual reviews simply can’t keep up with the ever-evolving tactics of fraud schemes reaching new levels of sophistication. As such, we’re at a crossroads that demands advanced technologies capable of outsmarting even the craftiest criminals.

The good news?

Breakthroughs in artificial intelligence (AI) and machine learning seem to be turning the tide in this high-stakes battle against fraud.

Companies now have access to AI systems that can mimic human cognition to sniff out emerging fraud like an expert investigator. These technologies are also lightning-fast, adapting on the fly to pinpoint suspicious activity across massive datasets in seconds.

AI’s Advantages in Fraud Detection

When it comes to outsmarting fraudsters, artificial intelligence packs some serious firepower. AI is equipped with special capabilities that allow it to wipe the floor with humans and old-school rules-based systems when detecting fraud.

AI Excels at Recognizing Hidden Patterns

Unlike rules-based systems, artificial intelligence has an innate ability to detect anomalies and subtle patterns associated with fraud.

Even if a fraud scheme is new, an AI system can often identify unusual data points or activities that signal something is amiss. The algorithms are so advanced that they pick up on patterns that even teams of human investigators would likely miss. AI can detect these precursor indicators and predict fraud methodologies before they are deployed at scale.

Analyzing Datasets Beyond Human Capabilities

Another advantage of AI is its ability to process massive volumes of transaction data to pinpoint fraud. An AI system can analyze millions of payment transactions, for example, and compare them against known fraudulent activity. Things that would take an army of humans weeks or months to review can be accomplished by an AI system in just minutes or hours. The scale of fraud datasets that can be processed and analyzed with artificial intelligence is simply beyond human capabilities.

Rapid Adaptation to Emerging Threats

On top of its lightning-fast data skills, AI also adapts at record speeds to detect new fraud tactics. Advanced machine learning models allow AI fraud fighters to instantly tweak themselves based on the latest threats. So if crafty bad actors rollout a new scheme, AI can quickly learn how to spot it and respond. The algorithms essentially upgrade themselves in real-time – giving AI the power to evolve even faster than the most sophisticated fraud can.

Lightning-Fast Processing Speeds

Finally, artificial intelligence allows for fraud predictions and decisions to be made at incredible speeds. By leveraging optimized machine learning models, AI-based fraud systems can analyze transactions and make determinations in milliseconds. This enables millions of transactions to be screened for fraud simultaneously. The ultra-fast processing empowers businesses to stop more fraud in progress, rather than after the damage is already done. This speed advantage is a complete game-changer compared to manual reviews or waiting for rules to be updated.

Key Machine Learning Methods for Fraud Detection

The fraud detection machine learning capabilities discussed below represent the primary approaches used to train AI systems for accurately identifying fraudulent activity.

Supervised Learning Operates Like Fraud Experts

One powerful machine learning technique used in fraud detection is supervised learning. Here, algorithms are trained on labeled datasets containing fraudulent and legitimate transactions. This allows the systems to learn the signals and patterns that distinguish fraud from normal activity – almost like having expert analysts training them. Algorithms like neural networks and support vector machines are commonly used for this. Once trained, these models can evaluate new transactions and predict if they are fraudulent or not.

Unsupervised Learning Finds Suspicious Outliers

Another method is unsupervised learning, where models must detect fraud from unlabeled datasets. Algorithms like clustering and anomaly detection are used to identify transactions that are outliers or deviate from normal patterns. This allows fraud to be flagged even if the system wasn’t trained on specific examples. Since fraud is an outlier activity, unsupervised learning excels at identifying unusual transactions.

Hybrid Models Combine the Best of Both

Many modern fraud systems use a hybrid approach combining supervised and unsupervised learning. This provides more robust detection capabilities. The supervised algorithms identify patterns learned from past fraud, while the unsupervised models detect new anomalies. Blending both techniques allows for accurate predictions along with the ability to detect previously unseen fraud tactics.

Online Learning Adapts in Real-Time

Some advanced systems apply online learning to fraud detection. These machine learning models continuously update to identify new fraud patterns in real-time. As new transactions are observed, the algorithms automatically tweak themselves to better detect emerging fraudulent activity. Online learning enables fraud detection that dynamically adapts to the latest tricks fraudsters have up their sleeves.

Deep Learning Takes it to the Next Level

On the cutting-edge, deep learning techniques, such as deep neural networks, are taking fraud detection to the next level. These systems can uncover extremely complex patterns and relationships across massive, high-dimensional datasets. Deep learning provides enhanced abilities to detect sophisticated fraud rings and organized criminal activity – even finding connections human investigators would likely miss.

Final Word

While some fear AI may one day become too powerful, for now it remains a tool, albeit an extraordinarily effective one.

By leveraging AI to bolster human intellect and diligence, we can create a formidable front against criminals who seek to steal, scam and defraud. The future looks bright for justice and consumer protection as AI assistance becomes more widespread and fraudsters find their craft made increasingly difficult.

The post The Role of AI and Machine Learning in Fraud Detection appeared first on AiThority.

Governments are often perceived as laggards when it comes to technology adoption. It’s not unusual to see many public offices still using hardware and software that are considerably older than what is popular in the market. To some extent, this is understandable, given the rigorous processes and requirements involved in making expenditure and acquisition decisions. However, this should not be an excuse not to keep up with technological advancement, especially in cybersecurity.

The cyber risks and attacks at present are not the same as the threats of yore. They are increasingly aggressive, complex, and pervasive. Their perpetrators are criminally ingenious and they can be state-sponsored, operating collaboratively to target specific governments, businesses, or organizations. Fortuitously, cybersecurity has a new ally: artificial intelligence. The catch: this ally may also work for the enemy.

Cybersecurity for governments

Before anything, what is cybersecurity for governments?

Is this a real category for cybersecurity or a mere marketing term? There are no established definitions for this term, even from the usual suspects when it comes to IT terminology coining like Gartner and McKinsey. However, security firms appear to recognize the special case of the need to secure government IT, so they are offering solutions that are specifically geared towards cybersecurity needs in the government setting.

There are many reasons why governments are a favorite target of cyber attacks. For one, maintain huge volumes of data that can be valuable to threat actors. Secondly, government offices tend to have weak security controls and practices, which makes them easy targets most of the time. Governments usually lack security proficiency, and it does not help that there is a continuing shortage of cybersecurity talent worldwide. Additionally, disruptions in government operations can also serve as “noteworthy” accomplishments for cybercriminals that are trying to establish their reputation.

As the recent geopolitical conflicts of the past few years demonstrated, state-sponsored attacks are not to be taken lightly. They are concerted, persistent, and sophisticated. Governments need security solutions that are effective but also easy to implement, scalable, and can be integrated with the legacy systems that are still commonly used in government offices.

How different are government cybersecurity needs from others?

Government cybersecurity requirements are usually comparable to those of larger enterprises with a multitude of endpoints, various types of assets, and complex infrastructure. The kind of protections needed in governmental organizations is not that different from those in the private sector. The most common requisite defenses include the following.

• Data security – This is the basic protection required in all levels of government operations. Governments collect and store vast amounts of sensitive data, from information about citizens to defense-related secrets, it is crucial to have the right data protection.
• Network security – All organizations that connect devices and connect to the internet require network security. It prevents threat actors from gaining a foothold in a government office’s network and hinders lateral movement attempts.
• Application security – All organizations that use modern devices use apps. As such, it is a must to have the appropriate application security controls and measures. This ensures that the apps facilitate service, not become tools for threat actors to attack individuals or government institutions.
• Endpoint security – Endpoints refer to all the devices that allow users to connect to the network and use resources or services. A comprehensive endpoint security system is vital for governments, especially because of the tendency of many in government to be careless about the devices they allow into their networks.
• Cloud security – Many governments are already using cloud services, so it makes sense to have this capability. Cloud security ensures that misconfigurations and third-party risks are avoided. This is particularly important for offices that are new to using the cloud or are acclimatizing to their hybrid infrastructure. It is important to have a system that can detect cloud security issues and ensure the protection of data and other assets hosted on the cloud.

How AI helps

Simply put, artificial intelligence helps governments improve their cybersecurity by making it easy to put the right security controls in place. Instead of headhunting top cybersecurity talents, government organizations can turn to AI-powered security solutions that provide comprehensive cyber defenses.

Many modern cybersecurity platforms incorporate artificial intelligence to boost threat detection, mitigation, remediation, and prevention. They can automate various manual processes to speedily detect and address threats. This automation frees up the limited cybersecurity professionals in government offices so that they can focus on high-level tasks that require complex decision-making that may only be relegated to humans.

One of the biggest benefits of AI in cybersecurity is its ability to go over tons of security-related data to contextualize them and reduce instances of false positives, failure of detection, and information overload. AI can set priorities for security alerts and event notifications to make sure that the most urgent concerns are addressed promptly, not concealed or buried under loads of benign notifications.

Another advantage of having AI for government cybersecurity is its ability to undertake advanced behavioral analytics. Instead of solely relying on threat intelligence and security event profiles, AI-backed cybersecurity solutions can scan network activity and establish benchmarks of normal or safe behavior. It can then run advanced behavioral analysis to spot cases of potential malicious behavior, which deviate from the benchmarks. This enables the detection of zero-day threats even if the security system is not yet aware of the new threats.

In addition to behavioral analysis, AI can also run predictive analytics to anticipate future attacks. With the help of the massive amounts of data regularly collected by government institutions and obtained from other sources, AI can look into trends and patterns in cyber attacks, thus helping them prepare countermeasures and plan resource allocations to prevent attacks or cope with the aftermath of a successful attack. AI can generate actionable insights to outwit cybercriminals.

Moreover, AI supports automated incident response. It helps governments swiftly and effectively respond to threats. It reduces the time it takes to detect and address attacks. If attacks manage to penetrate, AI can also guide how to minimize the impact of the attack and accelerate remediation.

Adversarial AI, legacy systems, and other challenges

The problem with AI is that it is not exclusive to cybersecurity use. Adversaries can similarly take advantage of it. This means that AI does not only revolutionize cyber defense. Unfortunately, it can also boost the capabilities of threat actors, as it helps cybercriminals in various ways. AI can rapidly generate new malware to be used in various attacks. It can be used to automatically scan systems for exploitable vulnerabilities. It can also automate attacks and find ways to successfully evade security controls.

Another challenge for government organizations is the continued use of legacy systems. Many still employ devices and software from a decade or more ago. The US Government Accountability Office (GAO) acknowledges this problem, the same with most other governments. However, the problem has persisted and appears unlikely to be resolved anytime soon. Fortunately, there are AI-powered cybersecurity platforms designed for this. They are capable of achieving comprehensive security visibility, even on legacy hardware and software.

Moreover, many governments face the challenge of having limited resources. Not many can afford to implement leading-edge security solutions. Also, the fragmented nature of their operations makes security visibility and management more challenging, plus they do not have enough cybersecurity skills.

One of the most viable solutions to these challenges is embracing AI. It is high time for governments to invest in AI literacy and AI-supported cybersecurity. Artificial intelligence is not a silver bullet or a one-size-fits-all solution, but it provides undeniable benefits not only in threat detection and prevention but also in incident response. It changes the way governments secure their IT assets given the changing threat landscape and the limited resources (including cybersecurity talent) of governments.

The post Securing the Digital Frontier: How AI Can Revolutionize Cybersecurity for Governments appeared first on AiThority.

Over the past week, Blizzard reported that their systems were repeatedly targeted with Distributed Denial of Service (DDoS attacks). Their servers went down and became available only for users in certain locations.

Players are getting frustrated because they can’t access many of the games they normally would. For many, the gaming experience has been negatively affected due to exasperating lag. Some users reported that their email addresses even got hacked in the midst of a DDoS attack.

On April 20, the company shared that its systems are still being targeted with DDoS threats on a daily basis.

Also known as botnet attacks, DDoS are common threats to businesses that rely on versatile applications and networks — meaning most companies operating today.

The longer a botnet attack compromises the company’s infrastructure, the more financial and reputational damage the business suffers in the long run.

What is a botnet attack exactly, and what is the role of artificial intelligence in avoiding, detecting, and ceasing this malicious cyber threat?

What Is a Botnet Attack?

Botnets are groups of devices that connect to the internet. Whether we talk about mobile, desktop or IoT devices, threat actors who control botnets (AKA botmasters) hijack them to initiate botnet attacks.

The users whose devices are being exploited by a botnet group are often not aware their computers and mobile phones are part of the process. Botmasters can use the same device to attack multiple networks at the same time.

Generally, botnets are deployed to spam a specific website or to crash entire servers. Hackers’ intentions can be to harm the company’s reputation, finances, or both.

How does a botnet attack happen, exactly?

A robotic army controlled by the online criminal is used to send a large volume of traffic to the victim’s network or application.

As a result, the company can lose access to its network, or their application might crash — depending on the capacity of a botnet and how much traffic is used to flood the target. 

The volume of DDoS attacks on the application level is measured in RPS (requests per second). On the network level, the attack is more severe and is measured in PPS (packets per second).

The attacks can last a few minutes, days, or even months. Depending on the hacker’s intention and the power of the botnet, the network or application can completely crash or slow down to the point where users get frustrated and leave the service.

AI-Powered Botnet Attack Protection

How to protect the network or an application from malicious botnet attacks? Due to the large volume and an increasing number of threats, cybersecurity teams delegate repetitive security tasks to artificial intelligence.

Some of the tasks that can be automated with the use of AI in cybersecurity include:

• Detection of signs of a cyberattack
• Analysis of data generated from the security tools
• Blocking of traffic that is deemed malicious
• Generating reports that depict the state of security and provide actionable tips on how security teams can mitigate the issues at hand

With AI, analysis of traffic and mitigation are possible in real time. The processes are repeated at all times, and security analysts have an insight into the state of security 24/7.

AI-Based DDoS Protection

To fight botnet attacks, cybersecurity teams rely on cloud-based DDoS attack prevention tools — they are designed to detect and block unwanted traffic.

How does DDoS protection work in practice?

It identifies a large number of versatile DDoS attacks — which is important since hackers are developing new and more complex methods every day.

For instance, that could mean the detection of attacks that occur on the application, Domain Name System (DNS) or network levels.

The traffic is inspected before reaching the network of a user. It’s compared with the ever-growing database that lists versatile hacking techniques and malicious IP addresses. Within the network, packets are triple-checked to ensure that the traffic is legitimate.

When the botmaster targets an application, the automated DDoS solution automatically identifies the signature of the botnet to differentiate it from genuine human activity.

Only the traffic that is deemed “clean”, genuine and safe will reach the system of a company. The rest are blocked.

Layered AI Cybersecurity Architecture

In many cases, a botnet attack is just the start. Threat actors tend to team them up with other hacking techniques. It goes without saying that companies today need a layered and comprehensive security system to protect themselves from such versatile and depleting attacks.

In the case of Blizzard, players shared that their email addresses got compromised during the DDoS attack that occurred.

DDoS attacks are also often paired with ransomware. Once the file-encrypting malware is deployed on the network and the ransom is requested, criminals can initiate DDoS attacks to add more pressure on their victims.

Therefore, having other automated security solutions that can detect and mitigate threats in time is essential. Most businesses have layers of 40–90 cybersecurity solutions to protect their most valuable assets. 

Final Word

Botnet attacks are difficult to eradicate completely. These “zombie armies” tend to come back every year — on a larger scale and more advanced than the year before.

As mentioned, even major enterprises such as Blizzard aren’t immune to DDoS attacks — let alone companies that don’t have the same resources but rely on applications and networks in their day-to-day.

To prevent and stop threats such as botnet attacks today, artificial intelligence has a key role in cybersecurity. AI can keep up with the incoming data and continually scan the traffic to detect malicious activity, such as a vast amount of traffic fast.

As companies are up against more cyber attacks than ever before, and threats are getting more and more sophisticated as well as hitting the servers with more volume, organizations have to prepare beforehand — with streamlined technology that can detect issues in real-time.

The post How Do AI-Based Cyber Tools Prevent and Mitigate Botnet Attacks? appeared first on AiThority.

Humans have experienced a unique relationship with weather.  In addition to living with the environment around us, people have discussed the weather as a classic small talk tactic, predicted weather using old knee injuries, and have been caught off guard when the weather was completely different than we expected.

With technology, modern society has taken away the mystery from more and more things. Case in point, you can watch your food delivery person on your phone in real-time, thanks to the smart computer that is your phone and a network of interconnected satellites orbiting far out in space. Arguably this isn’t why all this technology was created, but it is certainly an interesting side benefit.

That said, the weather continues to elude us in terms of having high accuracy.  We can predict the shipping of an item we bought halfway around the world, and most of the time, that estimate is correct with perhaps a day variance on either side.  But with the weather, we are not surprised when the forecast calls for sun, but later that afternoon, you are very much wishing you had brought an umbrella.

Artificial Intelligence or AI ML News: Iterate.ai Adds Generative AI Features to Interplay’s Low-code Environment

So, why is this happening?  Why have we made massive advancements in so many things but not in the weather forecast industry?  Well, we actually have made quite a bit of progress. Still, the critical point is this: thanks to advances in artificial intelligence (AI), more ways to acquire huge amounts of weather data, and the ability to compile and gain insights quickly, we are on the verge of gaining major leaps in understanding how our weather works, how climate change is affecting it, and how to predict it better.

Artificial Intelligence or AI’s Natural Strengths

If you are to believe some of the current headlines, AI seems to be everywhere, accomplishing unheard-of achievements and seemingly capable of anything.  While there have been major breakthroughs that continue to build on each other, AI is still complex, difficult, and in nearly all cases, frustratingly narrow-focused.  It is able to perform tasks and solve problems extremely well, assuming it has the right data, enough of it, and the boundaries of the problem are small enough.  We are a long way away from “general AI,” the type of artificial being that can perform wildly different tasks.

However, we don’t need general AI to solve many previously impossible problems.

Artificial Intelligence or AI tends to be extremely good at a handful of things, which are then applied creatively to countless use cases.  AI can detect patterns (e.g., image classification, object detection), use evolution-like behavior combined with structured reinforcement to learn new tasks (e.g., teaching a robot how to walk), and it can predict the near future (e.g., autocomplete, translation, regression).  This last element is critical to the problem of predicting the weather.   With enough data, even something as complex as the weather can be understood and predicted through the proper application of AI.

Weather’s Incredible Complexity

Weather is notoriously difficult to predict because it is an incredibly complex, interconnected, fluid system.  The term “the butterfly effect” is appropriate when discussing the weather because although large trends like average temperatures in a given location for a given month don’t change a great deal each year (excluding the effects of climate change), the day-to-day changes can vary significantly in terms of temperature, precipitation, pressure, wind, and other variables.  Because these variables are not consistent over even a small space and are constantly being mixed around in the fluid we call the atmosphere, change is constant, and prediction accuracy past a day or two drops significantly.

A key issue is that there are many constantly changing variables. While we can see correlations between them, it is difficult to assign causality between variables in a system this complex.  Even advanced statistical analysis can’t handle this many variables affecting a system that is only a “closed system” if you include the entire globe.

Breakthroughs On The Horizon

However, this is where AI comes into play.  It is exceedingly good at taking data sets with hundreds or thousands of features, all of which could be interacting with each other, and with the proper training, develop an understanding of how the variables influence each other to create a certain result.  In addition to the advances in AI algorithms, our ability to capture, store, and process vast amounts of data is also critical.

AiThority.com AI ML Insights: AiThority Interview with Pete Wurman, Director at Sony AI

For example, Lockheed Martin and NVIDIA are teaming to generate a “digital twin” of the current global weather, funded by the National Oceanic and Atmospheric Administration (NOAA).  This twin (a digital representation that uses data to recreate and mimic the original essentially) will have terabytes of data and will use AI to help the process, understand, and display it so that researchers can better understand on a global scale our entire weather system.

What is unique about our current state of the art is that it doesn’t take a Lockheed Martin to create insight, innovation, and breakthroughs for better weather prediction.  The two biggest hurdles—data and the proper code—are both available to anyone who wants them. For Artificial Intelligence, while it takes training to understand the algorithms and the process, nearly all the code is open source, and there are vast amounts of training material online.  For the weather data, there are now platforms like Tomorrow.io that offer a historical weather data API where users can connect and access vast amounts of detailed weather data in order to train AI models, then utilize the API to get current weather data.  This wide-open access will undoubtedly attract many talented developers, and the effort is very likely to result in new weather prediction breakthroughs.  We have the data, we have the AI, and we have the talent.

[To share your insights with us, please write to sghosh@martechseries.com]

The post AI and Open Access Weather Data Will Provide Groundbreaking Insights appeared first on AiThority.

The internet is a vast and ever-expanding place. With so much content available at our fingertips, it can be overwhelming trying to find what we’re looking for – not to mention the hours of scrolling, sifting, and searching required to get there.

This is especially true if you’re trying to discover lesser-known or niche content, or content that’s been around for a while but is no longer as popular.

AI and machine learning could be the answer to this problem. These technologies have the potential to revolutionize content discoverability and make it easier for users to find what they need by analyzing data and patterns in an efficient, effective way.

AIThority News: Promising Success: 5 AI Trends for Business Owners and CEOs

Not only will this benefit the average user, but researchers, content creators, and businesses alike can all benefit from the improved discoverability algorithms AI and machine learning offer. On that note, let’s take a closer look at how AI and machine learning can be used to improve content discoverability.

Understanding User Intent with Natural Language Processing (NLP)

One of the primary challenges that AI and machine learning can help with when it comes to content discoverability is understanding user intent. For example, search engines may be confused with ambiguous queries, or not be able to distinguish between the intent of a query and the context in which it was asked.

Natural language processing (NLP), which is a branch of AI that deals with the interaction between computers and human language, can help with this. NLP enables machines to interpret and understand language, enabling them to better comprehend user intent and surface the most relevant content.

Take a search query such as “where is the best place to buy a laptop.” With NLP, the machine will be able to understand that this query is about shopping for a laptop, and not, say, researching laptop manufacturers or finding the closest electronics store.

Personalized Recommendations with Machine Learning Algorithms

Machine learning is another branch of AI that enables machines to learn from data and past experiences, allowing them to make predictions about future trends or outcomes without the need for human intervention.

In terms of content discoverability, machine learning algorithms can be used to identify user preferences and offer personalized recommendations based on previous searches or interactions. This can help users find the most relevant content for their needs more quickly and efficiently, as well as discover new or unknown content that they might enjoy.

A good example of this in action would be how Netflix recommends movies and shows to users based on their viewing habits. With its machine learning algorithms, it can curate a selection of content that is tailored to each individual user – presenting them with a set of options that they might not have considered browsing on their own.

Recommended: Email Marketing Tools with AI features to Transform Your Business

Automated Tagging and Categorization

A lot of the content online can be difficult to find because it is not properly tagged or categorized. Most of the time, this is an error on behalf of the content creator or website administrator, but it can also occur due to outdated or incomplete metadata.

AI and ML offer an automated solution to this problem, by collecting data from the content itself and using it to identify keywords, phrases, or topics that can be used for tagging and categorization purposes. By understanding the context, machines can accurately identify the most relevant tags for the content – making it easier for users to find what they’re looking for.

This automated tagging and categorization can also help content creators save time by reducing the amount of manual tagging they have to do. It can also help ensure that the content is properly indexed and that users can find it when they search for relevant keywords.

Overcoming Challenges with Content Discoverability

While the potential benefits of AI and ML in content discoverability are clear, there are challenges associated with their implementation, such as:

Developing the Infrastructure 

There needs to be adequate infrastructure in place that can support the processing and analysis required for NLP and machine learning algorithms. Data lakes, data warehouses, and data processing solutions such as Snowflake and Databricks are just some of the tools that can be used to achieve this.

Organizations looking to implement AI/ML for content discoverability purposes must conduct Snowflake vs Databricks comparison analysis or other data platforms to determine which platform can best meet their specific needs, and which tools (or blend of tools), can best support the infrastructure.

Data Quality and Accuracy

AI and ML technologies rely on the accuracy of data in order to make reliable predictions and recommendations. If the data is outdated or inaccurate, it can lead to incorrect results. AI and ML models must be continuously tested and monitored to ensure that they are producing the correct results.

Privacy and Security

The use of AI and ML technologies for content discoverability will also raise privacy and security concerns. It is essential that organizations ensure that they have the necessary security measures in place to protect user data and that they are transparent about how they are using it.

Overcoming Bias in Algorithm Training Data

AI and ML algorithms must be trained on datasets that are representative of the user population. If the training data is biased, the algorithms may produce inaccurate results that reflect the bias in the data. Organizations must be aware of this and take steps to reduce any potential bias in the training data.

SaaS and Cloud News: The #1 Cloud ERP for the UHNW Market Launches New Features for 2023

Final word on Content Discoverability

AI and ML have the potential to revolutionize content discovery, enabling all of us to find what we are looking for quickly and with ease. Through automation of tagging and categorization, as well as personalized recommendations based on user preferences, it’s safe to say that the future of content discoverability looks bright.

However, organizations must take into consideration the challenges associated with implementing AI and ML technologies in order to ensure accurate results and maintain user privacy. By doing so, we can all enjoy a better content discovery experience without sacrificing our security.

The post Why AI and Machine Learning Could be the Answer to the Content Discoverability Conundrum appeared first on AiThority.