As data sharing becomes more prevalent due to the increased value it creates through collaboration in sectors like public healthcare, financial services, and digital governance, particularly with respect to sensitive data, businesses must remain vigilant about the risks to the confidentiality and privacy of this data. At present, most organisations hide or redact user information using human-centric “traditional data redaction” methods that operate within a set pattern of rules.
However, with the rise of AI and machine learning, attackers have an edge in seeking and exploiting vulnerabilities in a dynamic fashion, especially when data is on the other hand redacted through static means, therefore assessment of such methods becomes imperative. As per the reports by IBM and Stanford, human error is responsible for 88 to 95 percent respectively, for cyber-security incidents.
What lies ahead for businesses and how could they counter these threats better?
Future-proofing privacy: why businesses need to adapt
Traditional Methods have several drawbacks due to their straightjacket approach to redact data in situations where the risk scenarios are dynamic.
According to a 2019 survey report from Forrester Research:
- 80% of cyber-security decision-makers predicted that AI would improve the speed and magnitude of privacy breaches.
- While 66% believed that AI may carry out assaults that surpass human imagination.
- It also indicated that 74% of the respondents think that “IP or data theft” poses the most risk through an AI-powered attack on privacy.
The report claimed that future attacks would exhibit a stealthy and unpredictable character, posing challenges for conventional security measures that depend on predefined rules and patterns, and solely take into account previous incidents to effectively counteract them.
The infamous Netflix Prize incident serves as a perfect example of why conventional methods are inept for complete redaction, where researchers utilised techniques such as linkability, singling out and inference, able to identify users by studying their movie reviews, highlighting how lack of contextual awareness may negate privacy measures in documents.
The implication is clear here: to effectively adapt to the nature of current attacks, it is essential to utilize the same advanced technology that is being used offensively. This means employing AI-based tools to redact and anonymize sensitive datasets.
The extent and complexity of emerging threats businesses may have to face:
- Automated data harvesting and attack orchestration:
Attackers now use bots to scrap data off sources including publically available or shared documents by businesses and use AI to harvest and analyse data of consumers or end users out of these databases, which is easier once a traditional data redaction technique or algorithm method is identified and then reverse-engineered. This is especially important when attacks on businesses are being scaled up and made faster, making it extremely difficult for conventional methods to catch up to the techniques attackers use to de-anonymize data.
- Pattern recognition and link analysis:
AI can analyse the collected data to identify patterns and connections within the traditional redaction or masking technique used. Attackers can construct a thorough psychological profile of an individual by assembling fragments of information from documents obviating redaction or anonymization efforts. A significant factor that is to be highlighted here is the lack of contextual understanding of documents posited by conventional redaction techniques which allows for pattern recognition by malicious actors.
- Data leakage via metadata:
In some instances, metadata traces that include sensitive information may be overlooked through conventional methods of redaction within a facet of the dataset or documents (highlighting one of the aspects of human errors relating to conventional methods), which may expose this data to potential identification of principals.
- Dynamic nature of privacy laws:
Businesses may seem to face challenges in complying with privacy laws, both due to the vast amounts of rules and regulations within or across jurisdictions and the ever-evolving nature of them. Most significant of them being the GDPR or the CCPA which constantly evolves, changing the criteria of what kinds of information need to be redacted and the ways to do it. Most of the companies employ traditional methods of redaction, which may require substantial operational efforts and legal costs to understand and deal with the vast array of legal compliance requirements, which they either do not possess (about 35% of businesses in a 2023 survey) or when they do, decrease their profitability and efficiency nonetheless.
AI powered solutions as a better defense towards privacy
The nature of such emergent threats now makes it imperative that organisations balance the scales by evolving their strategy with AI driven solutions to counter these novel categories of attacks. Here is how AI-powered redaction, could effortlessly overcome conventional redaction methods:
- Dynamic threat awareness and counter-measures:
Artificial Intelligence possesses the ability to constantly adapt to the novel and sophisticated ways vulnerabilities could be discovered and exploited by malicious actors, and evolve equally potent measures to deal with them which is not efficaciously or economically viable with traditional human-centric approaches.
- Natural Language Processing (NLP) and contextual awareness:
In successful de-anonymization attempts, such as the Netflix demonstration, potential attackers exploited the lack of contextual awareness inherent in surface-level redaction to cross-relate and single out links to sensitive data.
AI based tools could ensure superior redaction, with the ability to process vast amounts of data and the sensitive information within it, with a deep contextual understanding of the role, words play as well as the relation between pieces of information with their NLP (Natural Language Processing) based AI.
- Multi-layered defense:
AI powered redaction transcends the normal limitations of conventional redaction techniques by layering multiple sophisticated methods like synthetic data substitution, tokenization and redaction through asterisks.
- Tokenization & Asterisk Substitution involves replacing sensitive data within documents with un-linkable “tokens” or asterisks respectively with intelligent and context-aware AI which renders it indecipherable to AI algorithms normally used by attackers to de-anonymize.
- Synthetic Data Substitution is a process that is preferred in datasets where the sensitive data sought to be redacted is mentioned and interlinked across the database and thus, has a contextual or statistical value within the wider scheme of the document or database concerned.
- Additional security mitigating breach through human errors:
AI driven tools are now capable of ensuring effective data security, with an additional level of security layer at the data level which ensures that redacted data has no intrinsic value. Therefore, even if such data is leaked through human error, it would not compromise the sensitive nature of the information or the privacy of concerned parties.
- Cost efficient compliance with privacy laws:
Organizations could save a bulk of their time and money earlier deemed as legal costs to ensure the privacy of their documents, with the multitude of privacy laws to be complied with, by using AI tools. AI obviates the need to stay constantly on the edge due to the volatile nature of legal compliance requirements and dedicate resources to do so, by making changes in its algorithms in conciliation with the laws in real time, thus, automating the whole process for businesses in a fraction of the cost.
With the threat scenario ever-evolving, where emerging technology allows malicious actors to find ways to mine and de-anonymize data never conceived before, businesses could benefit exponentially from upgrading their redaction efforts through AI, bolstering the privacy of their data as well as achieving it with a fraction of the cost and time they would have spent on it with conventional means staying updated across the privacy compliance spectrum, with laws and emergent capabilities of attackers.
