Protect sensitive information at rest, in transit, and during processing by using strong encryption algorithms. Encryption ensures the confidentiality and integrity of data, preventing unauthorized access or tampering. This mitigation can be implemented through the following measures: Encrypt Data at Rest: - Use Case: Use full-disk encryption or file-level encryption to secure sensitive data stored on devices. - Implementation: Implement BitLocker for Windows systems or FileVault for macOS devices to encrypt hard drives. Encrypt Data in Transit: - Use Case: Use secure communication protocols (e.g., TLS, HTTPS) to encrypt sensitive data as it travels over networks. - Implementation: Enable HTTPS for all web applications and configure mail servers to enforce STARTTLS for email encryption. Encrypt Backups: - Use Case: Ensure that backup data is encrypted both during storage and transfer to…
MITRE mitigation sourceAdversaryGraph public intelligence page
This page is part of Threat Matrix, the public browser workspace for the main AdversaryGraph platform. Use it for ATT&CK pivots, actor and technique context, similarity leads, detection coverage review, and analyst-ready investigation paths.
Validation disclaimer: TTP overlap, actor similarity, generated summaries, and coverage findings are investigation leads, not attribution proof or operational validation without analyst review.
Main AdversaryGraph project Documentation Malware Analysis GitHub
Data Manipulation
Adversaries may insert, delete, or manipulate data in order to influence external outcomes or hide activity, thus threatening the integrity of the data. By manipulating data, adversaries may attempt to affect a business process, organizational understanding, or decision making. The type of modification and the impact it will have depends on the target application and process as well as the goals and objectives of the adversary. For complex systems, an adversary would likely need special expertise and possibly access to specialized software related to the system that would typically be gained through a prolonged information gathering campaign in order to have the desired impact.
Open detection, hunting, mitigation, and evidence workspace
Detection logic
Where applicable, inspect important file hashes, locations, and modifications for suspicious/unexpected values. With some critical processes involving transmission of data, manual or out-of-band integrity checking may be useful for identifying manipulated data.
Observed actors
Correlated CTI and IR reports
Cyber Knowledge context
Use these routes to move from the ATT&CK behavior into explanation, implementation, evidence handling, validation, and defensive operations. Relevance is generated from explicit identifiers/names and governed topic mappings; it is not attribution evidence.
Digital Forensics & Incident Response (DFIR) · Tactic learning route · 24/100Policy architecture, standards, procedures, and exceptions
Governance, Risk & Compliance (GRC) · Tactic learning route · 24/100Module 6 — Alert triage, investigation, and escalation
Blue Team & Defensive Security · Tactic learning route · 24/100
MITRE mitigations
Remote Data Storage focuses on moving critical data, such as security logs and sensitive files, to secure, off-host locations to minimize unauthorized access, tampering, or destruction by adversaries. By leveraging remote storage solutions, organizations enhance the protection of forensic evidence, sensitive information, and monitoring data. This mitigation can be implemented through the following measures: Centralized Log Management: - Configure endpoints to forward security logs to a centralized log collector or SIEM. - Use tools like Splunk Graylog, or Security Onion to aggregate and store logs. - Example command (Linux): `sudo auditd | tee /var/log/audit/audit.log | nc <remote-log-server> 514` Remote File Storage Solutions: - Utilize cloud storage solutions like AWS S3, Google Cloud Storage, or Azure Blob Storage for sensitive data. - Ensure proper encryption at rest and access cont…
MITRE mitigation sourceNetwork segmentation involves dividing a network into smaller, isolated segments to control and limit the flow of traffic between devices, systems, and applications. By segmenting networks, organizations can reduce the attack surface, restrict lateral movement by adversaries, and protect critical assets from compromise. Effective network segmentation leverages a combination of physical boundaries, logical separation through VLANs, and access control policies enforced by network appliances like firewalls, routers, and cloud-based configurations. This mitigation can be implemented through the following measures: Segment Critical Systems: - Identify and group systems based on their function, sensitivity, and risk. Examples include payment systems, HR databases, production systems, and internet-facing servers. - Use VLANs, firewalls, or routers to enforce logical separation. Implement DMZ f…
MITRE mitigation sourceRestricting file and directory permissions involves setting access controls at the file system level to limit which users, groups, or processes can read, write, or execute files. By configuring permissions appropriately, organizations can reduce the attack surface for adversaries seeking to access sensitive data, plant malicious code, or tamper with system files. Enforce Least Privilege Permissions: - Remove unnecessary write permissions on sensitive files and directories. - Use file ownership and groups to control access for specific roles. Example (Windows): Right-click the shared folder → Properties → Security tab → Adjust permissions for NTFS ACLs. Harden File Shares: - Disable anonymous access to shared folders. - Enforce NTFS permissions for shared folders on Windows. Example: Set permissions to restrict write access to critical files, such as system executables (e.g., `/bin` or `…
MITRE mitigation sourceMITRE detection strategies and analytics
- AN0162 · Analytic 0162 — Correlate unauthorized or anomalous file modifications, deletions, or metadata changes with suspicious process execution or API calls. Detect abnormal changes to structured data (e.g., database files, logs, financial records) outside expected business process activity.
- AN0163 · Analytic 0163 — Detect unauthorized manipulation of log files, database entries, or system configuration files through auditd and syslog. Correlate shell commands that alter HISTFILE or data-related processes with abnormal file access patterns.
- AN0164 · Analytic 0164 — Detect manipulation of system or application files in `/Library`, `/System`, or user data directories using FSEvents and Unified Logs. Identify anomalous process execution modifying plist files, structured data, or logs outside expected update cycles.