Defending Against Gen AI-Based Hacking (GenAI Cybersecurity)

Identify the four attack mediums that gen AI-assisted hackers exploit and the characteristics, required data, and defenses examined for each.

Identify how gen AI supports attacks on wireless and network infrastructure, such as evil twin access points, WiFi phishing, and AI-coordinated DDoS, and select detection methods like wireless IPS, anomaly detection, and zero-trust access.

Recognize how gen AI supports attacks on endpoints through polymorphic malware, malicious scripts, fake security tools, ransomware, and privilege escalation, and apply defenses such as EDR, anomaly detection, patching, and application allowlisting.

Explain how gen AI helps attackers find and exploit misconfigurations such as open storage, over-permissive IAM, leaked API keys, and default credentials, and apply CSPM and infrastructure-as-code scanning to counter them.

Analyze gen AI-driven attacks on identity and authentication, including credential phishing, deepfakes, synthetic identities, and MFA fatigue, and select defenses such as phishing-resistant MFA, behavioral biometrics, and identity proofing.

Consolidate the understanding of how gen AI-assisted attacks target networks, endpoints, configurations, and identity systems, and how each can be detected.

Identify the four gen AI-assisted attack approaches and the process, impact, and prevention examined for each.

Describe how attackers use gen AI to find and exploit vulnerabilities in code and configurations, and apply defenses such as secure coding practices, secret detection, and continuous patching.

Explain how gen AI is used to build custom and polymorphic malware, assess its consequences, and apply defenses such as EDR, application allowlisting, PoLP, RBAC, and network segmentation.

Analyze how attackers use gen AI to identify detection rules and craft activity that evades them, and counter this with defense in depth, anomaly detection, honeypots, threat hunting, and dynamic thresholds.

Explain how data and model poisoning compromise AI systems and defensive models, and apply data provenance, adversarial testing, model auditing, and secure training pipelines to prevent them.

Consolidate the ability to compare gen AI-assisted attack approaches and match each with its consequences and prevention measures.

Identify the four defense strategies used to counter the speed and scale that gen AI gives attackers.

Apply moving target defense by continuously changing configurations, network paths, and environments to raise the cost and time of attacks, following best practices such as automation and unpredictability.

Design adaptive deception using dynamic honeypots, honeytokens, and deceptive network paths that evolve with attacker behavior to slow attackers and reveal their actions.

Apply zero-trust architecture principles such as strong identity verification, least privilege, network segmentation, and adaptive authentication to limit the blast radius of compromised credentials.

Defend AI models against prompt injection and adversarial inputs using input sanitization, adversarial training, guardrails, and isolation of connected systems.

Consolidate the ability to choose and combine defenses against gen AI-assisted attacks, from moving targets and deception to zero trust and model robustness.

Identify the four cybersecurity tasks that LLMs can help automate and the process, prompts, and trade-offs examined for each.

Use LLMs to analyze code and configuration files for vulnerabilities and generate risk-rated recommendations, weighing benefits against limitations such as hallucinations and missed flaws.

Use LLMs to correlate logs, summarize behavior, add context, and prioritize suspicious activity, while accounting for the risks of hallucination and slower processing.

Use LLMs to analyze dependencies, match versions to known CVEs, prioritize fixes, and plan upgrade paths, while recognizing their reliance on current vulnerability data.

Use LLMs to triage security events and trigger predefined defensive changes such as isolating devices, revoking sessions, or activating honeypots, while managing the high stakes and integration complexity.

Consolidate the ability to evaluate where LLM-based automation benefits cybersecurity tasks and where its limitations require caution.

Identify the five implementation topics covering changes to defenses, automation of scanning, access reviews, and incident response, and ethical considerations.

Distinguish which defenses remain essential and which new or adapted ones gen AI requires, such as AI-driven attack simulations, zero trust at the data layer, granular DLP, and credential abuse detection.

Integrate LLM-based vulnerability scanning into CI/CD pipelines by selecting review triggers, tracking KPIs, and keeping human oversight to avoid false confidence and data leakage.

Apply LLMs to zero-trust policies and periodic access reviews by cross-referencing context to spot access creep, while managing data privacy, prompt consistency, and integration with existing IAM systems.

Automate incident response and security tasks with LLMs by defining automation boundaries, standardizing prompt templates, and tracking metrics, while avoiding alert hiding and unvetted actions.

Assess the ethical risks of using LLMs in cybersecurity, including data privacy, accountability, model misuse, overreliance, and bias, and apply guardrails such as least privilege, red-teaming, AI policies, and audit logging.

Consolidate the ability to plan the implementation of gen AI defenses, from workflow changes and automation limits to ethical safeguards.

Review how attack mediums, attack approaches, defenses, and implementation practices combine into a strategy for protecting against and with gen AI.

Preview a systematic approach to judging generative AI outputs before relying on them in fraud prevention work.

Adopt a structured validation discipline that separates plausibility from truth and enforces judgment before trust.

Expose hidden assumptions, incentives, and directional framing that subtly push conclusions or actions.

Separate fluent narrative from verifiable evidence by demanding traceable sources and factual support.

Test whether the parts of an output align logically, structurally, and contextually across sections and iterations.

Challenge confident detail by asking whether the model realistically had the knowledge or authority to make such claims.

Identify what is missing (constraints, trade-offs, risks, or verification paths), that should naturally be present in a complete answer.

Consolidate a repeatable routine for validating generative AI outputs and spotting errors, bias, and manipulation.

Anticipate the Gen AI literacy topics developed in the module, including capabilities, limits, technical foundations, and adoption challenges.

Describe the current state of Gen AI, including foundation models and their uneven impact on work, and recognize the cognitive, emotional, and organizational shifts they introduce.

Classify Gen AI use cases by usefulness and risk, distinguishing tasks where it reliably augments work from those where it produces shallow reasoning, instability, or serious errors.

Evaluate how Gen AI shifts professional effort from execution toward automation, structured reasoning, orchestration, and new validation responsibilities.

Explain core technical concepts of Gen AI systems, including models, context, embeddings, and hallucinations, to interpret outputs, limitations, and failure modes.

Identify common organizational and human pain points in Gen AI adoption, such as unrealistic expectations, workflow misalignment, trust erosion, and cognitive degradation.

Consolidate Gen AI literacy on capabilities, limits, and risks as a foundation for applying it to loan underwriting.