Balancing functionality and privacy concerns in AI-based Endpoint Security solutions

The integration of Artificial Intelligence (AI) in endpoint security has revolutionized the way organizations protect their devices and data.

Ok, let’s take a break here: have you read the article about Artificial Intelligence vs. Machine Learning ?


By leveraging AI and machine learning models that analyze user behavior on devices, organizations can detect anomalies and potential security threats more effectively.

However, this advanced approach to endpoint security raises significant privacy concerns, as it necessitates the collection of user activity data, sometimes in real time.

One thing needs to be clear: if you want to do anomaly detection, you need to train your ML model with what “normal” is first – this is called “baseline”. And this means that data needs to be collected from the user.

Now the question remains, how can we reduce the privacy concerns?

This short article explores the privacy challenges I think are associated with using AI models that require user data(behavior), discusses potential solutions, and suggests ways to deploy AI on devices while minimizing privacy concerns.

What are the privacy concerns when data is collected for training an ML model?

Data Collection and Usage

Collecting user data for AI-driven endpoint security involves monitoring and logging user activities on devices.

This process includes:

  • capturing information about the applications used (URLs accessed, CPU usage, memory usage),
  • websites visited and items clicked
  • files accessed
  • applications installed
  • applications started
  • time of login, logout, inactivity
  • webcam usage
  • microphone usage
  • biometrics

This data is essential for creating baselines of normal behavior and identifying deviations that might indicate security threats.

This extensive data collection raises concerns about user privacy, as it creates a comprehensive profile of a user’s digital activities.

AI-based endpoint security solutions can infer or predict sensitive information from non-sensitive forms of data, such as user preferences, interests, or behaviors.

This can enable the systems to provide personalized or customized services or recommendations, but it can also violate the privacy or autonomy of the users or the owners of the devices or networks.

For example, someone’s keyboard typing patterns can be analyzed to deduce their emotional state, which includes emotions such as nervousness, confidence, sadness or anxiety


Data Security

Safeguarding the collected user data is critical, as it contains sensitive information about an individual’s online behavior.

The risk of data breaches or unauthorized access to this information poses a significant privacy threat.

Where is this data stored, how long, how is it stored, who has access to it, how is it going to be used/processed and by who, are just a few questions that need to be asked.

GDPR has made clear which are the responsibilities of the controller and processor(s) of the data.


Transparency and Consent

A good user experience of a security product means that users will be as unaware as possible that their activity data is being collected for security purposes.

Ensuring transparency and obtaining explicit user consent for data collection is critical. Without clear communication, users may feel their privacy is being violated.


Data Retention

Storing user data indefinitely can compound privacy concerns. Organizations should establish clear data retention policies, specifying how long the data will be retained and under what circumstances it will be deleted.


User Profiling and Discrimination

The detailed user activity data collected for AI analysis can lead to user profiling, which may be used for purposes beyond cybersecurity, such as targeted advertising.

AI-based endpoint security solutions can make automated decisions or recommendations based on the data they analyze, such as blocking access, flagging anomalies, or prioritizing alerts.

Discriminatory decisions and practices can arise from the insights drawn from user behavior data. However, these decisions or recommendations can be discriminatory, unfair, inaccurate, or biased, if the data or the algorithms are flawed, incomplete, or skewed.

For example, people can be misclassified, misidentified, or judged negatively, and such errors or biases may disproportionately affect certain demographics.


Solutions to address privacy concerns

The solutions to address these concerns are actually not new, they are covered pretty good by the GDPR and other privacy laws world-wide.

They are :

Data Minimization

Organizations should adopt a data minimization approach, collecting only the data necessary for security purposes.  This is definitely not as easy as it sounds.

In Security, you usually collect as much as possible, because the more you know about your target, the better it is for the ML model (better detection, less false positives).

However, the Compliance dept. should be involved from the early stages of developing the product in order to control what is being collected.



Anonymizing user data can be a privacy-enhancing technique. By removing personally identifiable information from collected data, the risk of individual users being identified is reduced.

This works good when data is collected from many computers, but when the solution works on a single computer, it usually needs time to “learn” the user’s behavior.

There is nothing anonymous there and this is usually OK, as long as this data is not sent to the backend for further processing and analysis.



Encrypting the data collected for AI analysis ensures that even if a breach occurs, the information remains unreadable and inaccessible to unauthorized parties.

When “cleaned up” data needs to be sent, it is mandatory to send it encrypted and keep it at rest encrypted all the time.


Informed consent

Transparently informing users about data collection and obtaining their explicit consent is a fundamental step in addressing privacy concerns.

Users should have the option to opt in or out of data collection at any time. It is mandatory for the ML models to be able to cope without any datasets, because they could disappear at any time.


Data deletion

After the data is no longer needed for security analysis, organizations can ideally erase the data, and if this is not possible, then it should remove any direct or indirect associations with individual users.

Balancing Security and Privacy

Balancing AI-based endpoint security and privacy is essential. Organizations can adopt the following strategies to minimize privacy concerns:

  • Implement Strong Privacy Policies

Establish comprehensive privacy policies that clearly define data collection, usage, retention, and disposal procedures. These policies should adhere to legal and regulatory requirements for the region where the users reside (GDPR, CPA, etc.).

This can by itself be a challenging task, because no company is willing to block access to potential customers.


  • Regular risk assessment and impact analysis

Conduct periodic risk assessment and impact analysis to ensure that data collection and analysis practices align with privacy policies and legal requirements and correct any deviations promptly.

The audits should be first performed internally, in order to have time to fix any deviations. If an external audit body finds any irregularity, the company can be fined with large sums of money.


  • Third-Party Vetting

When using third-party AI solutions, organizations should thoroughly vet the security and privacy practices of these providers.


  • Ongoing Monitoring

Continuously monitor the effectiveness of privacy protection measures and adjust them as needed to address emerging privacy concerns.



AI-based endpoint security is a powerful tool for protecting devices and data from cyber threats. However, it should not come at the cost of user privacy or well-being.

Organizations must strike a delicate balance by implementing privacy-enhancing measures, obtaining informed consent, and adhering to transparent data collection and usage practices.



PS: The image of the post was generated using DALL-E.


The post Balancing functionality and privacy concerns in AI-based Endpoint Security solutions first appeared on Sorin Mustaca on Cybersecurity.

Strengthening the Security of Embedded Devices

Embedded devices are specialized computing systems designed to perform specific tasks or functions within a larger system. Unlike general-purpose computers, embedded devices are typically integrated into other devices or systems and are dedicated to carrying out a specific set of functions. They are often characterized by their compact size, low power consumption, and optimized performance for their intended application.

Embedded devices can be found in various domains and industries, including consumer electronics, automotive, healthcare, industrial automation, telecommunications, and IoT (Internet of Things). Examples of embedded devices include:

  1. Smartphones and tablets: These devices integrate multiple functionalities such as communication, multimedia, and internet access into a portable form factor.
  2. Home appliances: Devices like refrigerators, washing machines, and thermostats may contain embedded systems that control their operations and offer smart features.
  3. Industrial control systems: Embedded devices are widely used in manufacturing plants and industrial environments to monitor and control processes, machinery, and equipment.
  4. Automotive systems: Embedded devices are essential components in modern vehicles, managing functions such as engine control, entertainment systems, safety features, and navigation.
  5. Medical devices: Embedded systems are utilized in various medical equipment, such as patient monitoring devices, implantable devices, and diagnostic tools.
  6. IoT devices: These are interconnected devices that gather, transmit, and process data. Examples include smart home devices, wearable devices, and environmental sensors.

Embedded devices typically consist of hardware components (such as microprocessors, memory, and sensors) and software (including operating systems, firmware, and application software) tailored to perform specific tasks efficiently. They are designed to operate reliably in often resource-constrained environments and are subject to specific security and safety considerations based on their application domain.

Overall, embedded devices serve as the backbone of numerous technological advancements, enabling automation, connectivity, and enhanced functionality in various sectors.

Embedded devices have become an integral part of our daily lives, powering everything from smartphones and smart home devices to critical infrastructure and industrial systems. However, their proliferation also brings forth significant security concerns. Ensuring the security of embedded devices is of paramount importance to protect against potential vulnerabilities and mitigate the risks of cyber threats. This article explores the key challenges surrounding the security of embedded devices and highlights the measures needed to fortify their defenses.

The Unique Security Challenges:
Embedded devices face several unique security challenges that differentiate them from traditional computing systems:

1. Resource Constraints: Many embedded devices have limited computational power, memory, and energy resources. This poses challenges in implementing robust security mechanisms without impacting the device’s performance or battery life.

2. Long Lifecycles: Embedded devices often have long lifecycles, meaning they remain in operation for extended periods. Ensuring security over such durations necessitates proactive measures, including regular software updates and patch management.

3. Diverse Ecosystems: Embedded devices interact with a diverse range of software and hardware components, creating a complex ecosystem that requires careful consideration of security across all layers, from hardware to firmware and software.

Enhancing Security in Embedded Devices:
To bolster the security of embedded devices, the following measures should be implemented:

1. Secure Booting: Enforcing secure booting mechanisms ensures that only trusted and authenticated software components are loaded during the boot process. This prevents the execution of unauthorized or malicious code, establishing a foundation of trust in the device’s software stack.

2. Code and Data Encryption: Implementing strong encryption algorithms safeguards sensitive data stored on embedded devices, as well as the communication channels they utilize. Encryption helps protect against unauthorized access and data breaches, ensuring the confidentiality and integrity of the device and its data.

3. Robust Authentication: Strong authentication mechanisms, such as multifactor authentication or biometrics, should be employed to verify the identity of users or external systems attempting to access or interact with the device. This prevents unauthorized access and reduces the risk of compromise.

4. Regular Software Updates: Timely and regular software updates are crucial for patching security vulnerabilities and addressing emerging threats. Embedded device manufacturers should provide updates throughout the device’s lifecycle, ensuring that security patches and fixes are deployed promptly.

5. Secure Communications: Implementing secure communication protocols, such as Transport Layer Security (TLS) or Virtual Private Networks (VPNs), protects data transmitted between embedded devices and external systems, safeguarding against interception and tampering.

6. Vulnerability Management: Regular vulnerability assessments and penetration testing should be conducted to identify and address potential weaknesses in embedded devices. This proactive approach helps identify and remediate vulnerabilities before they can be exploited by attackers.

7. Secure flashing: regular software updates don’t bring too much if there are no mechanisms to ensure that the updates are authentic. This mechanisms checks that the delivered updates are signed by the producer of the device and therefor secure to deploy.

We will be addressing in several articles some of these unique challenges they present : secure booting, implementing encryption and authentication, software updates, secure flashing, secure communications, vulnerability management.


The post Strengthening the Security of Embedded Devices first appeared on Sorin Mustaca on Cybersecurity.

Preventing Attacks and Securing the Supply Chain in the Security Software Industry

The security software industry plays a vital role in safeguarding sensitive data and protecting digital infrastructure.

However, the industry itself faces a significant threat from supply chain attacks.

Supply chain attacks occur when cybercriminals target vulnerabilities within the supply chain to compromise software or hardware products before they reach the end-users.

By infiltrating the supply chain, attackers can inject malicious code, backdoors, or vulnerabilities, thereby compromising the security of the software.

Such attacks can have far-reaching consequences, as they can compromise the confidentiality, integrity, and availability of critical systems and data.

These attacks have the potential to undermine the integrity and trustworthiness of security software, leading to severe consequences for individuals, organizations, and even nations.

This article examines the damaging impact of supply chain attacks on the security software industry, while also delving into preventive measures and strategies to secure the supply chain.



  1. Loss of Trust: Supply chain attacks erode trust in security software products and the industry as a whole. When high-profile incidents occur, customers may lose confidence in the ability of software vendors to protect their assets and data.
  2. Financial Loss: The costs associated with supply chain attacks are staggering. Companies suffer significant financial losses due to reputational damage, legal consequences, customer compensation, and the costs of investigating and mitigating the attack.
  3. Weakened Defenses: A compromised security software product can result in weakened defenses for individuals, organizations, and governments, leaving them vulnerable to further cyberattacks. This situation can have severe consequences, particularly when critical infrastructure or national security is at stake.


Preventing Attacks:

  1. Enhanced Vendor Due Diligence: Organizations should thoroughly vet and assess the security practices of their software vendors and suppliers. This includes scrutinizing their security measures, incident response plans, and third-party audits.
  2. Secure Development Practices: Implementing secure software development practices, such as code review, vulnerability testing, and penetration testing, can help identify and rectify potential weaknesses in software products.
  3. Strong Authentication and Encryption: Implementing robust authentication mechanisms and encryption protocols helps protect the integrity and confidentiality of software and its supply chain components.
  4. Regular Updates and Patching: Ensuring timely and regular updates and patches are applied to software products and their supply chain components helps address known vulnerabilities and protect against emerging threats.



  1. End-to-End Visibility: Organizations must have comprehensive visibility into their supply chain, including the identification of all suppliers and the ability to monitor their security practices throughout the software development lifecycle.
  2. Supply Chain Risk Assessment: Conducting a thorough risk assessment helps identify potential vulnerabilities and risks within the supply chain. This assessment should encompass all stages, from design to distribution, and involve evaluating suppliers, their security practices, and their access controls.
  3. Supplier Contracts and Agreements: Organizations should establish clear contractual agreements with suppliers that define security requirements, incident response protocols, and breach notification obligations. Regular audits and assessments can help ensure compliance.
  4. Incident Response Planning: Developing and regularly testing an incident response plan specific to supply chain attacks enables organizations to respond swiftly and effectively, mitigating the impact of any potential breach.


Supply chain attacks pose a significant threat to the security software industry, compromising the integrity and trustworthiness of software products. The damaging consequences include loss of trust, financial losses, and weakened defenses. However, by implementing preventive measures, such as enhanced vendor due diligence, secure development practices, and regular updates, organizations can bolster their defenses against supply chain attacks. Additionally, securing the supply chain through end-to-end visibility, risk assessments, supplier contracts, and incident response planning.


If you want to know how to address Supply Chain issues, you can contact Endpoint Cybersecurity for a free consultation.

Supply Chain Management



The post Preventing Attacks and Securing the Supply Chain in the Security Software Industry first appeared on Sorin Mustaca on Cybersecurity.