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NEW QUESTION # 66
What does watermarking AI generated content prevent?
Answer: C
Explanation:
In the realm of Artificial Intelligence and DevSecOps,watermarkingis a critical security technique used to identify the origin of synthetic media. As generative AI models become increasingly sophisticated, they can create highly realistic images, videos, and audio clips-often referred to asdeep fakes. These deep fakes pose a significant risk to organizational security and public trust, as they can be used for sophisticated social engineering attacks, such as impersonating executives in "Business Email Compromise" (BEC) scenarios or spreading misinformation.
By embedding a cryptographic or perceptible watermark into AI-generated content, security systems and users can verify the authenticity and provenance of the media. This proactive measure helps prevent the successful deployment of deep fakes by making it easier for automated security tools to flag synthetic content that lacks a valid "signature" of origin. While watermarking does not inherently stop the creation ofharmful content(Option C) or reduceresource consumption(Option A), it provides a layer of accountability and verification. Similarly,scale changes(Option D) are technical image manipulations that watermarking does not prevent. Within the Cisco SDSI framework, watermarking is viewed as an essential component of the AI security lifecycle, ensuring that generative technologies are used responsibly and that synthetic content is distinguishable from genuine data.
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NEW QUESTION # 67
A financial company is focused on proactively protecting sensitive data stored on the devices.
The company recognizes the potential risks associated with lost or stolen devices and they want a solution to ensure that if unauthorized user access the device, the data it contains is not accessible or misused. The solution includes implementing a strategy that renders data unreadable without user authentication. Which solution meets the requirement?
Answer: D
Explanation:
Disk encryption ensures that data stored on devices is unreadable without proper authentication.
If a device is lost or stolen, unauthorized users cannot access or misuse the data, since encryption protects the contents until the legitimate user provides credentials.
NEW QUESTION # 68
A product manager is focused on maintaining the security integrity of a microservice-based application as new features are developed and integrated. To ensure that known software vulnerabilities are not introduced into the product, it is crucial to implement a robust application security technique. The technique must be applied during the build phase of the software development lifecycle, which allows the team to proactively entity and address vulnerability risks before deployment. Which application security technique must be applied to accomplish the goal?
Answer: D
Explanation:
Container scanning during the build phase ensures that no known software vulnerabilities are introduced into the application. It scans container images for outdated libraries, misconfigurations, and security flaws before deployment, allowing proactive remediation. This directly supports maintaining the security integrity of a microservice-based application.
NEW QUESTION # 69
An agricultural company wants to enhance the cybersecurity posture by implementing a defense- in-depth strategy to protect against polymorphic malware threats. Currently, the company's security infrastructure relies solely on a stateful traditional edge firewall that does not provide adequate protection against malware variants. Which technology must be added to the company's security architecture to achieve the goal?
Answer: C
Explanation:
A heuristics-based Intrusion Prevention System (IPS) analyzes traffic behavior and patterns, allowing it to detect and block polymorphic malware that constantly changes its signature to evade traditional defenses. Adding this technology strengthens the company's defense-in-depth strategy beyond the limitations of a stateful firewall.
NEW QUESTION # 70
What is a use for AI in securing network infrastructure?
Answer: C
Explanation:
In the architecture of modern security, Artificial Intelligence (AI) and Machine Learning (ML) are leveraged to move beyond reactive, signature-based defenses. One of the most significant uses of AI in securing network infrastructure is the detection ofzero-day attacks(often referred to in exam contexts as "day zero" attacks). A zero-day attack exploits a vulnerability that is unknown to the software vendor or the public, meaning no signature exists for traditional firewalls or antivirus software to block it.
AI identifies these threats throughbehavioral analysisandanomaly detection. By establishing a highly granular baseline of "normal" network traffic patterns-including flow direction, packet size, inter-packet arrival times, and protocol behavior-AI models can detect subtle deviations that indicate a malicious exploit.
For example,Cisco Secure Network Analytics(formerly Stealthwatch) andEncrypted Threat Analytics (ETA)use ML to identify the cryptographic "fingerprints" of malware even within encrypted traffic, without the need for decryption. This allows the security infrastructure to identify and mitigate threats at the moment they appear, rather than waiting for a vendor to release a signature. While load balancing (Option B), traffic shaping (Option C), and Quality of Service (Option D) are critical for network performance and availability, they are traditional traffic engineering functions that do not inherently provide the advanced threat detection capabilities offered by AI-driven security models. Within the Cisco SDSI objectives, AI is positioned as the primary technology for achieving proactive visibility and reducing the "Mean Time to Detect" (MTTD) for previously unseen vulnerabilities.
NEW QUESTION # 71
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