NIST Protect - Protective Technology

Protective Technology:

  • Tools and processes that support the “Protect” function of the NIST Cybersecurity Framework, which focuses on limiting the impact of potential security incidents. Here are a few key threats:

1. Phishing and Social Engineering

  • Risk: Absence of email filtering, user awareness training, and anti-phishing measures.
  • Impact: Credential theft, malware delivery, or unauthorized access.
  • NIST Control: Use email protection and user training.

2. Data Breaches

  • Risk: Weak data encryption, poor access controls, and lack of data loss prevention (DLP).
  • Impact: Unauthorized access to sensitive data, financial loss, and reputational damage.
  • NIST Control: Encrypt data at rest and in transit and implement access controls.

3. Ransomware Attacks

  • Risk: Lack of strong endpoint protection, backup integrity, and malware detection.
  • Impact: Systems and data are encrypted, leading to operational shutdowns and ransom demands.
  • NIST Control: Implement continuous monitoring and endpoint security.
 

Mitigation Strategies Using NIST Frameworks:

Encrypt Data in transit:
  • Use strong encryption algorithms (e.g., AES-256) to protect stored data.
  • Ensure backup data stored in SaaS platforms is encrypted to prevent unauthorized access.
Implement email & DNS (Domain Name System – internet) filtering:
  • Layered approach to blocking malware, SPAM, Zero-day exploits, malicious domains and more.
  • DNS Filtering encrypts all data to/from the internet. Also ensures users are browsing the internet safely & productively by enforcing policies to block inappropriate or non-work-related websites (e.g., adult content, gambling, or social media).
  Endpoint Security:
  • Antivirus and Anti-Malware: Detects and removes viruses, worms, Trojans, ransomware, and other malware.
  • Endpoint Detection and Response (EDR): Provides continuous monitoring and analysis of endpoint activities to detect, investigate, and respond to advanced threats, such as zero-day attacks and fileless malware.
  • Offers forensic capabilities for incident investigation.
  • Behavioral Analysis and Machine Learning: Analyzes endpoint behavior to identify unusual or malicious activities.
  • Uses machine learning to detect new, evolving threats that do not match known signatures.