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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-Assisted Security | 24% | - Security Operations Enhancement
|
| Topic 2: AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| Topic 3: Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
| Topic 4: Securing AI Systems | 40% | - AI System Protection
|
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問題 #54
A security operations center (SOC) has a very high volume of logs and alerts. The manager proposes the implementation of machine learning (ML) system to help with triage. Which of the following tasks is most suitable?
答案：D
解題說明：
Machine learning is best suited for analyzing large volumes of security data and distinguishing between true threats and false positives. By identifying and classifying alerts, the ML system helps the SOC prioritize incidents and reduce analyst workload.
問題 #55
An engineer is analyzing findings from a penetration test that indicate insufficient data encryption. The report also indicates that additional controls must be placed on the data. To protect against loss of intellectual property, the engineer must implement data security.
Part 1: Use drop-down menu to select the most appropriate protocol or cipher for each system component.
Part 2: Use the drop-down menu to select the most appropriate technique to apply to the modified data.
答案：
解題說明：
Explanation:
Part 1 - Select the protocol or cipher
System component
Select
API gateway
TLS 1.2
Database storage
CAMELLIA
AI model storage
HMAC-SHA512
Part 2 - Select the data-protection technique
Enter the techniques from the first row to the fifth row in this exact order:
Row Data modification Select
1 The name field is removed, while patient_id and dob remain De-identification
2 ip_addr and name are removed, leaving cookie and uid Anonymization
3 The card number is replaced by 0x0193828829 Tokenization
4 sensitivity: " SECRET " is added Classification
5 Part of the PIN is replaced by XXXX Masking
See Explanation
Why these are the intended answers
API gateway # TLS 1.2
The API gateway handles data moving between the client and backend systems. TLS is the only listed transport-security protocol and therefore protects the data in transit . OWASP recommends encrypting sensitive data in transit by using TLS with secure parameters.
Database storage # CAMELLIA
CAMELLIA is a symmetric block cipher suitable for encrypting substantial quantities of stored data. It is therefore the valid storage-encryption choice among the listed options.
Do not select AES-512 . AES-512 is not a standardized AES variant. NIST defines only AES-128, AES-192, and AES-256 .
AI model storage # HMAC-SHA512
The scenario separately emphasizes protection against loss or modification of intellectual property. HMAC- SHA512 supplies message authentication and integrity verification, allowing unauthorized modification of the AI model to be detected. HMAC is not confidentiality encryption, but it is the most appropriate listed additional control for protecting model integrity. OWASP specifically identifies HMACs as cryptographic controls for stored-data integrity.
Why the other options are not selected
* SHA-2 512: Hashing only; no secret key and no encryption.
* Elliptical curve: A family of cryptographic techniques, not a specific storage or transport protocol.
* ECDH: Used for key agreement, not direct bulk-data encryption.
* AES-512: Invalid AES key size.
* RSA-2048: Primarily used for asymmetric encryption, signatures, or key transport; inefficient for bulk database or model encryption.
* CAMELLIA: Appropriate symmetric encryption for stored data.
* HMAC-SHA512: Integrity and authenticity, not confidentiality.
Final Part 2 order
* De-identification
* Anonymization
* Tokenization
* Classification
* Masking
Explanation:
De-identification removes an explicitly identifying field-in this case, the person's name-while retaining other fields needed for processing. The remaining patient ID and date of birth could potentially permit re- identification when combined with other information, so this is de-identification rather than complete anonymization. OWASP describes de-identification as deleting, scrambling, or pseudonymizing direct and indirect identifiers.
Anonymization removes identifying attributes so that the displayed record is not directly tied to the named individual. Here, both the name and IP address are removed.
Tokenization substitutes a sensitive value with a non-sensitive representative token. The original payment- card number is replaced with the hexadecimal-style value 0x0193828829. OWASP recognizes tokenization as a method for reducing exposure of stored sensitive data.
Classification labels information according to sensitivity. Adding sensitivity: " SECRET " does not conceal or transform the PIN; it categorizes the record so appropriate controls can be applied. OWASP recommends classifying processed, stored, and transmitted data and applying controls based on that classification.
Masking obscures only part of a sensitive value while preserving its general format. Changing 999-99-9999 to 999-99-XXXX is partial masking.
問題 #56
An attacker successfully completes a denial-of-service (DoS) attack through the context window of an AI system. Thousands of characters are obfuscated and hidden behind an emoji.
Which of the following techniques best mitigates this type of attack?
答案：D
解題說明：
Basic Concept: Context window DoS attacks flood an LLM ' s context with obfuscated content to exhaust processing resources or manipulate model behavior. Attackers may hide large amounts of text behind Unicode characters like emojis. CompTIA SecAI+ Study Guide identifies prompt filtering as the primary defense against input-based attacks on LLMs.
Why D is Correct: A prompt filter inspects incoming inputs before they reach the LLM, detecting and blocking malicious content including obfuscated text hidden behind Unicode characters or emojis. By analyzing input structure, character counts, hidden content, and encoding anomalies, prompt filters can identify and reject attacks that attempt to abuse the context window, preventing resource exhaustion.
Why A is Wrong: Fraud detection systems are designed to identify fraudulent transactions or activities in structured data contexts. They are not designed to inspect LLM prompt structures for obfuscated content attacks on context windows.
Why B is Wrong: LLM-as-a-judge uses a secondary LLM to evaluate the quality or safety of another model ' s outputs. It operates post-generation and cannot prevent a DoS attack that occurs during input processing before output is generated.
Why C is Wrong: Pattern recognition can identify known attack patterns but requires the attack to match pre- learned patterns. Novel obfuscation techniques using Unicode or emoji hiding may evade pattern-based detection without dedicated prompt filtering logic.
問題 #57
A security administrator needs to improve an AI model. During an initial investigation, the administrator notices that two successive login failures are recorded every day, and then a successful login occurs after a specific time interval. All the successful login attempts have been during office hours.
Which of the following techniques should the administrator use to improve the AI model ' s security?
答案：B
解題說明：
Basic Concept: Pattern recognition is an AI technique that enables a system to identify recurring sequences or structures within data. In cybersecurity, detecting behavioral patterns such as consistent pre-login failure sequences followed by successful access is critical for threat detection. CompTIA SecAI+ Exam Objectives cover this under AI-assisted security.
Why B is Correct: The scenario describes a highly regular, repeating behavioral pattern - two failures followed by success at a specific time interval, consistently during office hours. Pattern recognition enables the AI model to learn this sequence and flag it as indicative of credential stuffing or an automated brute-force attack with timing controls. ML-driven pattern recognition is specifically designed for such behavioral anomaly detection.
Why A is Wrong: Access management controls who can log in and under what conditions. It enforces authorization policies but does not analyze or detect suspicious behavioral sequences in authentication logs.
Why C is Wrong: Signature matching compares known attack signatures against observed data. The described pattern is behavioral and time-based rather than a known malware or exploit signature, making this technique unsuitable.
Why D is Wrong: Vulnerability analysis identifies weaknesses in systems and code. It does not analyze authentication log sequences or detect behavioral patterns in user activity data.
問題 #58
A security administrator must provide access controls for AI systems to list tables. Which of the following should the administrator implement?
答案：A
解題說明：
Since the requirement is to control which users or systems can list tables, the proper control lies at the data access level. Implementing data access controls ensures only authorized entities can view or query the underlying tables used by the AI system.
問題 #59
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