Results for “bias-mitigation”

5 skills

Federal Organizational Conflict of Interest (OCI) sweeper backed by FAR Subpart 9.5 (9.501-9.508) and the active Theseus workspace knowledge graph. USE WHEN the user asks about OCI risk on a bid, organizational conflicts, incumbent conflicts, biased ground rules, unequal access to information, impaired objectivity, or any pre-bid OCI due diligence. Pulls `company`, `incumbent`, `subcontractor`, `customer`, `program_office`, and prior-contract relationships from the workspace KG, classifies each potential conflict into one of the three FAR 9.505 classes (biased ground rules, unequal access, impaired objectivity), and emits a structured findings envelope with mitigation recommendations (firewall, NDA, recusal, novation). DO NOT USE FOR FAR clause coverage audit (use `compliance-auditor`), proposal prose (use `proposal-generator`), competitor research (use `competitive-intel`), or pricing (use `price-to-win`).

More results

detecting-mimikatz-execution-patterns

Hunt for Mimikatz execution using command-line patterns, LSASS access signatures, binary indicators, and in-memory detection of known modules.

24.6k · bundle

analyzing-indicators-of-compromise

Triages and enriches indicators of compromise (IPs, domains, file hashes, URLs, email artifacts) from phishing emails, security alerts, or threat feeds, assigning confidence scores and dispositions using VirusTotal, AbuseIPDB, MalwareBazaar, and MISP.

24.6k · bundle

conducting-man-in-the-middle-attack-simulation

Simulates man-in-the-middle attacks using Ettercap, mitmproxy, and Bettercap in authorized environments to intercept, analyze, and modify network traffic for testing encryption enforcement, certificate validation, and detection capabilities.

24.6k · bundle

detecting-data-and-model-poisoning

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

24.6k · bundle