Social listening frameworks in Singapore: why the publicly available data exemption is not a free pass
Singapore’s PDPA is the most permissive data protection framework in Southeast Asia for social listening, thanks to its exemption for publicly available data. But “permissive” does not mean “unregulated.” Organisations that treat the exemption as blanket permission to collect, store, and analyse social media data without governance are exposing themselves to compliance risk — particularly after the PDPC’s October 2025 enforcement action against Marina Bay Sands, which used turnover-based penalty calculations for the first time.
What the publicly available data exemption actually covers
The PDPA’s publicly available data exemption allows organisations to collect personal data that an individual has deliberately made public without obtaining consent. On social media, this covers posts, comments, and profile information shared on platforms where the user has chosen public visibility settings.
The critical nuance is conditionality. If an individual changes their privacy settings to restrict visibility, the exemption ceases to apply. Data that was publicly available when collected may no longer qualify if the individual subsequently made it private. For social listening buyers, this means historical datasets must be periodically reviewed against current privacy settings — a capability most global tools do not offer.
The exemption also does not override other PDPA obligations. Organisations must still ensure data accuracy, implement reasonable security arrangements, limit retention to what is necessary, and restrict use to purposes a reasonable person would consider appropriate. The 2020 amendment’s legitimate interests exception provides an additional pathway, allowing processing where organisational benefit outweighs adverse effect on the individual — but this requires documented assessment, not assumption.
Why “We Are GDPR Compliant” is not enough
The most common compliance gap among global social listening vendors is the assumption that European GDPR compliance automatically satisfies Singapore requirements. It does not. The PDPA has distinct provisions that do not map cleanly to European frameworks.
Singapore’s Do Not Call Registry creates specific obligations around marketing communications that have no direct GDPR equivalent. The PDPA’s consent framework differs from GDPR’s in both scope and exceptions. And Singapore’s penalties are structured differently from GDPR’s — for breaches of data protection provisions, organisations face fines of up to 10 percent of annual turnover in Singapore (for those exceeding SGD 10 million in local turnover) or SGD 1 million, whichever is higher, while DNC-related violations involving dictionary attacks and address-harvesting software carry a separate cap of 5 percent of turnover (for those exceeding SGD 20 million) or SGD 1 million.
The Marina Bay Sands enforcement action in October 2025 was significant because it was the first time the PDPC applied turnover-based penalty calculations. Over 500,000 patron records were exposed. For social listening vendors handling large volumes of personal data, this precedent significantly increases the potential cost of non-compliance.
Five questions every social listening buyer should ask
First, where is the data stored? Singapore does not mandate data localisation, but many public sector and financial services organisations have internal policies requiring data to remain within approved jurisdictions.
Second, how does the vendor handle data retention and deletion? The PDPA requires organisations not to retain personal data longer than necessary. If your vendor stores historical social media data indefinitely, you need to understand how that aligns with your retention policies.
Third, what security certifications does the vendor hold? ISO/IEC 27001 and ISO 9001 certifications provide independently audited evidence of compliance with information security and quality management standards. Most global social listening vendors lack these certifications.
Fourth, how does the vendor’s AI process personal data? With AI-powered sentiment analysis and audience intelligence, the PDPA’s provisions around automated decision-making become relevant.
Fifth, does the vendor have local regulatory expertise? A vendor with Singapore-based operations and clients in regulated sectors will understand compliance constraints that a global platform configured remotely cannot match.
How Mandatory DPO Appointments Change the Buying Process
Since June 2025, Singapore organisations meeting prescribed thresholds must appoint a Data Protection Officer. This changes the social listening procurement dynamic because the DPO must be involved in vendor evaluation from the outset — not brought in after a tool has already been selected.
For social listening buyers, this means the evaluation criteria now formally include data governance capabilities: audit trails, access controls, retention management, and evidence of compliance infrastructure. Vendors that can demonstrate ISO-certified security, granular data controls, and local regulatory expertise will clear DPO review faster than those requiring extensive due diligence on foreign data handling practices.
Building a compliance-first social listening strategy
The practical approach starts with governance rather than features. Map your social listening objectives against existing data protection policies. Define what data you actually need to collect, establish the lawful basis for collection, set retention periods, and determine access controls. These governance decisions should drive vendor requirements, not the other way around.
Then evaluate vendors on compliance infrastructure with the same weight you give to dashboard design and data coverage. Isentia holds dual ISO certifications — ISO/IEC 27001:2022 for information security management and ISO 9001 for quality management — providing independently audited evidence of compliance. With nearly two decades of operations in Singapore and a client base spanning government agencies and financial institutions, Isentia understands the specific compliance constraints these sectors face.
In a regulatory environment where enforcement is accelerating and penalties are shifting to turnover-based calculations, the compliance foundation of your social listening programme matters more than any feature on a comparison chart.
Learn More
• Isentia Social Listening for Singapore — See how integrated monitoring covers Singapore’s multilingual media landscape across 6,000,000+ data sources.
Nikita Gundala manages brand marketing and thought leadership for Pulsar Group across the SEA and ANZ markets. With over three years of first-hand experience in the influencer marketing and PR industries, she specializes in translating real-time insights and audience intelligence into actionable content. Nikita holds a master’s in Marketing and Digital from ESSEC Business School, Singapore. She has contributed to the wider industry conversation by co-authoring articles and reports for The Business Times Marketing Interactive.
Would you trust a brand more if an AI model recommended it? For many, the answer is yes – and it’s changing the very nature of PR & Comms.
Our latest report digs into the changing nature of trust, as audiences turn to AI models for quick answers instead of going to organisations or media outlets directly, with AI fast becoming the final stop in the comms cycle.
This report unpacks:
Why trust has shifted, and where audiences are having these conversations
Why AI has become the last stop in the comms cycle
Methods for staying on top of your brand trust and reputation
To access the full report, fill in the form below:
If you ask ChatGPT or Gemini about your organisation today, the answer won't come straight from your website. Instead, it uses sources the model already trusts, which are often months or years old. So if your last big mention was a crisis or a controversy from 2023, that's probably still how AI describes you.
This is the tough reality for anyone working in PR and communications today. More people are getting their first—and sometimes only—impression of your organisation from an AI-generated summary, not from search results or the homepage. And these summaries often rely on outdated information.
What does freshness actually mean?
Content freshness refers to how recent the sources are that an AI model uses when it talks about you. It might seem like a minor technical point, but it's actually very important.
Search engines have always valued fresh content, and they let you update information quickly. If you change a page, Google recrawls it, and rankings can shift in days. Large language models don't work like this. As Lisa Main, Director at Main Bureau, said on Isentia's "AI as a Stakeholder" panel, "large language models are not databases of verified facts." These models are trained on a snapshot of the internet, updated only from time to time, and they rely on sources that were already prominent when they were trained. This means a past crisis or a controversy that is already resolved can keep showing up in AI answers long after it's no longer relevant.
She shared the example of how a day and a half after a notorious terror attack, she asked ChatGPT if the area had ever experienced a tragedy of that type. It replied that it had not." The model wasn't being careless, but it just hadn't updated to include the latest news. This gap between what reality is and what AI still believes is true sums up the content freshness problem.
Dr Nici Sweaney, founder of AI Her Way, explained on the same panel why this gap matters. She calls AI "an accidental narrator" — it shapes what people believe about your organisation just by repeating the latest information it received. The system simply uses what's available and is not trying to be harmful, so it's important to make sure that information is up to date.
How does this change the way organisations show up?
For PR and communications teams, this changes what "reputation management" means. Put simply, messaging that an LLM cites will remain relevant, no matter when it dates from. Messaging that has not been factored into the LLM’s answers, meanwhile, will have no discernible impact on an increasingly vital - even central - channel, regardless of how many other metrics it might win out on.
This leads to two important things to consider:
First, the conditions that surround recent earned media, statements, and announcements determine whether an AI model updates its picture of the brand, or keeps running on an outdated one. Catherine Arrow of the PR Knowledge Hub made a related point on the "Inside the AI Shift" webinar: LLMs and the agents built on them are "often forbidden from going behind paywalls, from scraping particular sites," which she said creates a kind of "news vacuum." The same logic applies to the brand’s own newsroom or press page. If it isn't feeding the model something current, the model has nothing current to draw from.
Second, owned content—like blog posts, media releases, and website pages — are strategically important because they’re something the organisation in question can control , but only if they are updated. If a page hasn't changed in eighteen months, it's much more likely to disappear from AI results, making any reputation built on it unstable. If something is published once and not updated, the brand risks letting older, less positive stories take its place.
For public sector and government communicators, the stakes are more immediate again. When a government agency's guidance changes, whether that's eligibility criteria, compliance requirements, or a service update, and the fresh version doesn't make it into what AI models are citing, people will still get fed old information, with potentially devastating real-world implications.
The evidence is already there
This is not just in theory. It's playing out in global research and in the day-to-day data right now.
AI is quietly replacing the front door to your content
The Reuters Institute's Digital News Report Australia 2026 confirms that many PR teams have noticed that Google organic search traffic to news sites dropped by a third worldwide between November 2024 and November 2025, and by 38% in the US, as AI Overviews and AI Mode launched. Publishers expect this traffic to nearly halve again in the next three years. Some now call this trend a move towards "Google Zero." For communications teams, this means people are increasingly less likely to click through to your website to check if information is current. More often, they're trusting what the AI says: hence why it’s so important to monitor content freshness.
AI models are now web-enabled and they might not actually guarantee source accuracy
One challenge is that most major chatbots are now web-enabled. For example, ChatGPT can browse the internet, Gemini uses Google Search, and Perplexity has its own live index. This makes it easy to assume that AI always knows the latest information. However, this does not mean that they are always accurate when it comes to citations. A study from Columbia's Tow Center for Digital Journalism tested eight AI search tools with 1,600 queries. They found that these tools failed to correctly identify or cite the source article more than 60% of the time. Some tools were wrong on most tests and rarely showed any uncertainty. New information has not had time to be checked or confirmed like older stories have. This is the real risk of relying on the newest updates — a story that is fast moving and poorly sourced about your organisation might end up in an AI answer before it’s even verified or fact-checked.
People are turning to AI chatbots specifically for what's new
The same report found that 35% of people who use AI chatbots for news do so to get the latest media updates. Dr Sora Park from the University of Canberra's News and Media Research Centre explained on the "Digital News Report Australia 2026" webinar that the main reason people use AI chatbots for news is that "AI collates stories from different news sources into a single response." People expect these tools to provide current information. If your organisation's newest content isn't included (and you have something current or novel to communicate) you miss the chance to reach audiences when they're most interested.
Fresh content doesn’t always equate to ‘new’ content
A notable example of creating freshness that LLMs reward and prioritise comes from updating existing pages, rather from creating brand-new content. Republishing and refreshing current material is more effective than many communications teams realise, as long as one actually updates the content, not just the date.
Evergreen pages are the first casualties when AI overviews roll in
The DNR Australia 2026 report also notes that once someone is inside an AI chatbot conversation, they rarely leave it to check the source — only 4% of AI chatbot users say they always or often click through to the original article, compared with 19% for search and 17% for social media. The pages that used to earn traffic just by sitting there, permanent and useful, are now the ones most likely to lose visibility, because AI models favour what's recent over what's merely correct.
One fresh statement doesn't automatically undo a stale narrative
If an executive online, especially one who has a lot of weight to what they post online, says something controversial and it quickly spreads across media articles, social media and search — it will definitely be picked up by AI as well. There is a golden window of opportunity that they need to capitalise on to clarify what they said. If they don’t, the negative story that was already built into the data AI models use, will not be affected much by the executive’s clarification statement, which wasn’t that timely anyway. As Catherine Arrow of the PR Knowledge Hub said on the "Inside the AI Shift" webinar: "public relations and media relations are not the same thing," and relying on a single release misses the point. The real lesson is not to publish faster after a crisis, but to build a strong, up-to-date presence before you need it. In our latest report, “How can leaders communicate in an age of scrutiny”, we’ve outlined exactly how comms leaders can communicate by adapting their content to audiences exposed to the “AI way” of news dissemination.
What PR & Comms teams should actually do?
The challenge is that organisations can't make an AI model update its answers whenever they want. What they can do is track whether recent work is actually being noticed, which is what Lumina AI View can help with.
Lumina AI View monitors which sources AI models use when talking about your organisation, how strong and recent those sources are, and how you compare to competitors. Freshness is one of five key factors in the overall score. If your freshness score drops, it's an early warning that your latest campaign or announcement hasn't reached the AI ecosystem yet, and older stories are still dominating.
What’s important to note is that the tool provides a list of source citations, paired with reputation pillars like direction, integrity, performance and innovation — giving a comms professional a fully-rounded understanding of what they need to do. It’s not just the case of knowing source citations, but also of understanding your own AI perception and performance to make informed decisions — whether that’s for a brand,a government agency, a NFP or elsewhere.
This kind of tracking is even more important because it shifts by industry and by market, so "AI visibility" doesn't mean the same monitoring job for every organisation. AI answers for healthcare might draw from the smallest, highest-trust pool of sources (mostly clinical and government), but SaaS and fintech answers lean heavily on editorial reviews and comparison sites. Ngaire Crawford made a similar point regionally on the "AI as a Stakeholder" panel. For the APAC region specifically, she pushed back on the assumption that editorial media dominates AI citations — "there are a lot of really massive claims about the impact of editorial media... some as high as 85, 88%. That's not what we're seeing." Instead, she found "a fairly even split between (editorial media) and company content," alongside a real presence for review sites, forums, and academic sources. For a comms team, that means the freshness strategy that works for a media-heavy consumer brand might not work for a government agency whose AI visibility is really riding on review sites, .gov pages, or industry forums instead.
By tracking regularly — weekly or as a routine check— you turn the vague concern of "what is AI saying about us" into something that is super clear. You can see if recent coverage changed your list of citations, or if your owned content is still being found, or where there are gaps that need to be filled because old stories still exist and are causing problems.
The opportunity in staying current
There's a real advantage here too. If old content keeps you tied to an outdated story, fresh content is a direct way for PR and communications teams to influence how AI presents them. Publishing regularly, keeping your own pages updated, and getting recent, credible coverage is not just for human audiences. It's how PR professionals can make sure the systems shaping first impressions have the right information.
Teams that make it an ongoing habit of checking in regularly, watching for changes, and keeping fresh, credible content flowing, will have more control over how AI describes their organisation.
If you would like to know more about our Lumina suite, please reach out here and our team will get in touch with for you a quick demo.
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Blog
Why is content freshness the new currency for AI visibility?
AI summaries are replacing websites as your organisation’s first impression. Here’s why content freshness—and the sources feeding these models—matters more than ever.