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Second Sem > Information Assurance - 1 > Flashcards

Information Assurance - 1 Flashcards

1
Q

a term used to describe the large volumes of data

A

BIG DATA

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2
Q

three types of big data

A
  1. structured
  2. unstructured
  3. semi-structured
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3
Q

Big data contains terabytes and petabytes of information that traditional database systems cannot handle.

A

VOLUME

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4
Q

Big data comes in all types of formats - from structured numeric data to unstructured text documents, email, video, audio, stock ticker data and financial transactions.

A

VARIETY

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5
Q

Big data is generated at an extremely fast pace, in real-time. It must be processed in real-time to capture the full value and insights.

A

VELOCITY

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6
Q

With big data also comes issues of data quality, consistency and reliability.

A

VERACITY

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7
Q

The ways in which big data can be used and formatted.

A

VARIABILITY

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8
Q

The real value of big data is what organizations can analyze from it to gain insights and competitive advantages, optimize operations and processes, personalize services and make smarter, data-driven decisions.

A

VALUE

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9
Q

6 Characteristics of Big Data

A
  1. Volume
  2. Variety
  3. Velocity
  4. Veracity
  5. Value
  6. Variability
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10
Q

This annual report analyzed over 23,000 security incidents and found that 36% of breaches involved insiders, highlighting the significant threat posed by insider activities in big data environments.

A

VERIZON DATA BREACH INVESTIGATIONS REPORT (2022)

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11
Q

According to this report, the finance and insurance sectors experienced the highest number of cyber attacks in 2022, primarily due to the large volume of sensitive data they handle

A

IBM X-FORCE THREAT INTELLIGENCE INDEX (2023)

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12
Q

This study found that the average cost of a data breach in 2022 was $4.35 million, with the cost increasing as the size of the breached data set increased.

A

PONEMON INSTITUTE’S “COST OF A DATA BREACH REPORT” (2022)

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13
Q

STUDIES AND FACTS BIG DATA BREACH

A
  1. GARTNER’S “TOP STRATEGIC TECHNOLOGY TRENDS FOR 2023”
  2. CLOUD SECURITY ALLIANCE (CSA) “BIG DATA ANALYTICS FOR SECURITY INTELLIGENCE” (2021)
  3. IBM X-FORCE THREAT INTELLIGENCE INDEX (2023)
  4. PONEMON INSTITUTE’S “COST OF A DATA BREACH REPORT” (2022)
  5. VERIZON DATA BREACH INVESTIGATIONS REPORT (2022)
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14
Q

This research paper highlighted the potential of big data analytics in enhancing cybersecurity by enabling organizations to detect and respond to threats more effectively.

A

CLOUD SECURITY ALLIANCE (CSA) “BIG DATA ANALYTICS FOR SECURITY INTELLIGENCE” (2021)

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15
Q

The sheer volume of data generated and processed in big data environments makes it challenging to secure effectively.

A

DATA VOLUME

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16
Q

Gartner identified cybersecurity mesh as one of the top strategic technology trends for 2023, emphasizing the need for a distributed and composable approach to security in environments with diverse data stores and processing locations.

A

GARTNER’S “TOP STRATEGIC TECHNOLOGY TRENDS FOR 2023”

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17
Q

The high speed at which data is generated, ingested, and processed in big data environments can make it challenging to keep up with security requirements and real-time threat detection

A

DATA VELOCITY

18
Q

Big data systems handle structured, semi-structured, and unstructured data from various sources.

A

DATA VARIETY

19
Q

With big data, access privileges are often granted to a large number of users, administrators, and analysts, increasing the risk of insider threats, such as unauthorized data access, misuse, or theft.

A

INSIDER THREATS

20
Q

Inadequate implementation of robust security measures, such as data encryption, access controls, auditing, and monitoring mechanisms, can leave big data environments vulnerable to breaches and unauthorized access.

A

LACK OF ROBUST SECURITY MEASURES

21
Q

Ensuring compliance with data privacy regulations and industry-specific security standards can be challenging in big data environments, where data is distributed and processed across multiple systems and locations.

A

COMPLIANCE AND REGULATORY CHALLENGES

22
Q

The intricate nature of big data architectures, comprising multiple components, technologies, and tools, introduces complexities in maintaining end-to-end security and ensuring seamless integration of security measures.

A

COMPLEXITY OF
BIG DATA ARCHITECTURES

23
Q

The lack of skilled professionals with expertise in big data security can hinder the effective implementation and management of security measures in these complex environments.

A

SECURITY SKILLS GAP

24
Q

Causes of Big Data Vulnerabilities

A
  1. DATA VOLUME
  2. DATA VARIETY
  3. DATA VELOCITY
  4. COMPLEXITY OF BIG DATA ARCHITECTURES
  5. INSIDER THREATS
  6. LACK OF ROBUST SECURITY MEASURES
  7. SECURITY SKILLS GAP
  8. COMPLIANCE AND REGULATORY CHALLENGES
25
Q

Tools to Prevent Threats

A
  1. GOVERNANCE AND COMPLIANCE TOOLS
  2. BIG DATA SECURITY ANALYTICS TOOLS
  3. VULNERABILITY ASSESSMENT AND PENETRATION TESTING TOOLS
  4. DATA LOSS PREVENTION (DLP) TOOLS
  5. SECURITY INFORMATION AND EVENT MANAGEMENT (SIEM) TOOLS
  6. DATA MASKING AND ANONYMIZATION TOOLS
  7. ACCESS CONTROL AND IDENTITY MANAGEMENT TOOLS
  8. DATA ENCRYPTION TOOLS
26
Q

Can protect data at rest and in transit, ensuring that even if data is accessed or intercepted, it remains unintelligible to unauthorized parties

A

DATA ENCRYPTION TOOLS

27
Q

Can help implement granular access controls, manage user identities and privileges, and enforce data governance policies across big data platforms.

A

ACCESS CONTROL AND IDENTITY MANAGEMENT TOOLS

28
Q

Can collect and analyze security logs, detect potential threats, and provide real-time monitoring and alerting capabilities for big data environments.

A

SECURITY INFORMATION AND EVENT MANAGEMENT (SIEM) TOOLS

29
Q

Can obfuscate sensitive data, such as personally identifiable information (PII), by masking or anonymizing it, reducing the risk of data breaches and ensuring compliance with data privacy regulations.

A

DATA MASKING AND ANONYMIZATION TOOLS

30
Q

Can identify, monitor, and protect sensitive data from unauthorized access, misuse, or exfiltration, both within the organization and across various channels.

A

DATA LOSS PREVENTION (DLP) TOOLS

31
Q

```

Can help identify vulnerabilities in big data platforms, applications, and infrastructures, enabling organizations to proactively address security gaps and weaknesses.

A

VULNERABILITY ASSESSMENT AND PENETRATION TESTING TOOLS

32
Q

Can leverage big data technologies and machine learning to detect and respond to advanced cyber threats, anomalies, and suspicious activities within big data environments.

A

BIG DATA SECURITY ANALYTICS TOOLS

33
Q

Can help organizations establish and enforce data governance policies, monitor data lineage, and ensure compliance with various regulations and industry standards.

A

GOVERNANCE AND COMPLIANCE TOOLS

34
Q

Impact on Cybersecurity

A
  1. INSIDER THREATS
  2. INCREASED ATTACK SURFACE
  3. DATA PRIVACY CONCERNS
  4. ADVANCED PERSISTENT THREATS (APTS)
  5. CYBERSECURITY ANALYTICS
  6. THREAT INTELLIGENCE
  7. USER BEHAVIOR ANALYTICS
  8. COMPLIANCE AND GOVERNANCE
35
Q

The distributed nature of big data environments, with data stored and processed across multiple locations and systems, expands the potential attack surface.

A

INCREASED ATTACK SURFACE

36
Q

Big data environments often involve granting access privileges to numerous users, administrators, and analysts, increasing the risk of insider threats.

A

INSIDER THREATS

37
Q

The complexity of big data architectures and the high volume of data can make it easier for APTs to go undetected.

A

ADVANCED PERSISTENT THREATS (APTS)

38
Q

On the positive side, big data technologies and analytics can be leveraged to enhance cybersecurity capabilities.

A

CYBERSECURITY ANALYTICS

39
Q

Big data can be used to gather and process threat intelligence from multiple sources, enabling organizations to stay informed about emerging threats, vulnerabilities, and attack vectors, and proactively implement appropriate security measures.

A

THREAT INTELLIGENCE

40
Q

By leveraging big data analytics, organizations can monitor and analyze user behavior patterns, detect anomalies that may indicate potential insider threats or compromised accounts, and respond promptly to mitigate risks.

A

USER BEHAVIOR ANALYTICS

41
Q

Big data tools and platforms can aid in data governance, monitoring, and auditing, helping organizations comply with various cybersecurity regulations and industry standards.

A

COMPLIANCE AND GOVERNANCE

42
Q

The vast amount of data collected and processed in big data environments, including personal and sensitive information, raises significant data privacy concerns.

A

DATA PRIVACY CONCERNS

Second Sem (8 decks)

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