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Unlocking the Energy of Community Telemetry for the US Public Sector

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Unlocking the Energy of Community Telemetry for the US Public Sector


Co-authors: Lou Norman and Erich Stokes

Understanding Community Telemetry

In Half 1 of this weblog sequence “Defining Community Telemetry” we outlined community telemetry and famous that it’s a transformative instrument for the US Public Sector because it supplies complete insights into community efficiency, safety, and utilization patterns. Telemetry information is sort of a Golden Nugget for a prospector as a result of it holds immense worth in uncovering hidden insights inside a community.

By accumulating and analyzing telemetry information, public sector organizations can achieve visibility into their community operations, which is important for environment friendly and automatic community administration. This visibility permits for proactive menace detection, efficiency optimization, and knowledgeable decision-making concerning useful resource allocation and community planning.

Community telemetry may tremendously scale back response time by giving correct info on what an contaminated host has communicated with, each on premises and to the Web. This enables responders to know what was compromised and simply as essential, what was not compromised. Community telemetry performs a vital position in enhancing safety response instances by offering detailed insights into community actions. Right here is the way it works:

  • Actual-Time Monitoring: Conventional community monitoring strategies usually depend on periodic polling, which may miss essential occasions. In distinction, telemetry supplies real-time information, permitting for speedy detection of anomalies and potential threats.
  • Complete Visibility: Telemetry presents a unified view of community actions, integrating information from numerous sources comparable to community gadgets, cloud providers, and purposes. This complete visibility helps in figuring out compromised hosts and understanding the scope of an assault.
  • Proactive Menace Detection: By constantly streaming information, telemetry allows proactive menace detection and sooner response instances. This strategy reduces the imply time to decision from hours to seconds, as operators are notified of points as they happen.
  • Enhanced Safety Operations: Telemetry information helps automated community administration and safety operations, permitting for environment friendly menace detection and response. This consists of figuring out what was compromised and making certain that unaffected programs stay safe.

Now let’s dive deeper to raised perceive Community Telemetry.

Diving Deeper

NetFlow and IPFIX (Web Protocol Stream Data Export)
NetFlow and IPFIX are protocols designed to supply detailed insights into community site visitors flows. These protocols allow organizations to watch and analyze the information traversing their networks, providing a complete view of communication patterns and information alternate volumes.

  • NetFlow, developed by Cisco, aggregates site visitors into flows based mostly on a set of key fields comparable to supply and vacation spot IP addresses, supply and vacation spot port numbers, and protocol kind. This aggregation permits for the identification of distinctive classes between gadgets, offering useful details about community customers, purposes, and site visitors routing.
  • IPFIX, however, is an IETF normal that extends the capabilities of NetFlow by providing a extra versatile and extensible framework for exporting move info. It helps a variety of knowledge varieties and might be custom-made to satisfy particular monitoring wants. Each protocols are instrumental in community administration, enabling organizations to detect anomalies, optimize efficiency, and guarantee safety.

By understanding who’s speaking with whom and the quantity of knowledge being exchanged, organizations could make knowledgeable choices about useful resource allocation, community planning, and safety measures. These insights are essential for sustaining environment friendly and safe community operations.

NetFlow Safe Occasion Logging (NSEL)

NSEL is a specialised safety logging mechanism constructed on NetFlow Model 9 know-how, particularly designed for firewalls just like the Cisco ASA sequence.

NSEL supplies a stateful IP move monitoring methodology that exports data indicating important occasions in a move, comparable to move creation, teardown, and denial. This stateful monitoring permits for an in depth evaluation of the move’s lifecycle, capturing the transitions and modifications in state that happen throughout its existence. This consists of monitoring Community Handle translation (NAT) and Port Handle Translations (PAT) connections by the firewall to grasp end-to-end connections throughout a translated boundary.

By specializing in these important occasions, NSEL presents a extra environment friendly and focused strategy to logging, decreasing the quantity of knowledge whereas nonetheless offering essential insights into community exercise.

NSEL is especially useful for monitoring and analyzing firewall exercise as a result of it captures and exports information about move standing modifications, which are sometimes indicative of safety occasions. As an illustration, flow-denied occasions can spotlight potential safety threats or coverage violations, whereas flow-create and flow-teardown occasions can present insights into regular and irregular site visitors patterns.

Moreover, NSEL helps the era of periodic flow-update occasions, which give byte counters over the move’s period, providing a extra complete view of community utilization. This detailed logging functionality allows community directors to raised perceive and reply to safety incidents, optimize firewall efficiency, and guarantee compliance with safety insurance policies. By integrating NSEL with instruments like Cisco Safe Workload, organizations can additional improve their safety posture by automated coverage enforcement and superior menace detection.

Cisco’s Encrypted Visitors Analytics (ETA)

ETA is a groundbreaking know-how designed to detect threats in encrypted site visitors with out the necessity for decryption, thereby preserving privateness and sustaining safety. Conventional strategies of menace inspection usually contain bulk decryption, evaluation, and re-encryption, which might be resource-intensive and compromise information privateness. ETA, nevertheless, circumvents these challenges by using superior telemetry and machine studying methods to research encrypted site visitors.

ETA extracts 4 primary information components from encrypted site visitors: the Sequence of Packet Lengths and Occasions (SPLT), the Preliminary Knowledge Packet (IDP), byte distribution, and TLS-specific options. These components present insights into the conduct of the site visitors with out revealing the precise content material.
By analyzing these information factors, ETA can determine anomalies and potential threats, comparable to malware, inside encrypted streams. This strategy not solely enhances safety by detecting threats in real-time but in addition ensures that the integrity of the encrypted information is maintained, as there isn’t any have to decrypt the site visitors. This revolutionary answer leverages Cisco’s community infrastructure experience, offering organizations with enhanced visibility and cryptographic compliance with out compromising on efficiency or privateness.

Encrypted Visibility Engine (EVE)

To construct upon ETA, Cisco has developed EVE for its Subsequent Era Firewalls (NGFW) to supply extra safety and visibility for encrypted site visitors. Like ETA, EVE can determine artifacts in encrypted site visitors comparable to the applying that’s operating and decide if this site visitors is benign or malicious with out decryption. Firewalls generally make choices based mostly off the applying that’s operating, and having the visibility to see this in encrypted site visitors makes the firewall extra environment friendly and far simpler to handle.

Community Visibility Module (NVM)

NVM is a part of Cisco Safe Shopper that focuses on accumulating detailed telemetry information from endpoint gadgets. It’s designed to supply complete insights into endpoint conduct and community interactions, that are essential for sustaining strong safety postures.

NVM captures wealthy move context from endpoints, whether or not they’re on or off the premises, and supplies visibility into network-connected gadgets and person behaviors. This telemetry information consists of details about person site visitors course and quantity, the vacation spot of that site visitors, software program processes and purposes current on the endpoint, and particulars concerning the machine itself, comparable to machine kind, working system, and community interfaces. NVM will permit the investigator to tie the NetFlow site visitors not solely to the endpoint IP tackle, however to the particular course of and father or mother course of on the endpoint. This enables the investigator to make the most of the tip person safety context that the method is operating below on the endpoint permitting for extra granular evaluation.

NVM makes use of the IPFIX protocol to seize, format, and transport this telemetry information to move collectors or community administration programs for evaluation and logging. This makes Cisco Safe Shopper the one safety agent for mobility that leverages IPFIX for endpoint safety telemetry.

The information collected by NVM is then analyzed to supply safety visibility and insights, which can be utilized for capability and repair planning, auditing, compliance, and safety analytics. By integrating with options like Cisco Safe Community Analytics or third-party platforms offered by Splunk, NVM allows organizations to watch utility use, classify logical teams of purposes, customers, or endpoints, and determine potential anomalies, thereby enhancing the general safety and administration of their IT infrastructure.

The Distinctive Benefit of Cisco {Hardware}

Cisco’s community {hardware} stands out in its potential to generate complete telemetry information throughout numerous community elements. For instance, NetFlow/IPFIX is embedded into Cisco Unified Entry Knowledge Aircraft (UADP) ASIC {hardware} for the reason that introduction of the CAT 3850 and all of the Cat 9K switches (Since that is embedded into {hardware}, Cisco can generate NetFlow/IPFIX with out including any CPU load on the machine. This can be a large profit for Cisco {hardware}) . These ASICs are integral to the efficiency and suppleness enabling superior options comparable to enhanced safety producing full NetFlow/IPX at line fee. Whether or not it’s routers and switches offering NetFlow information and ETA, firewalls providing NSEL insights and EVE, or endpoints delivering NVM information, Cisco ensures that each a part of your community contributes to a holistic view of your community atmosphere.

Conclusion

In conclusion, NetFlow and IPFIX are pivotal protocols that present detailed insights into community site visitors flows, enabling organizations to watch and analyze information traversing their networks. Telemetry information is sort of a golden nugget for a prospector as a result of it holds immense worth in uncovering hidden insights inside a community. By leveraging these protocols, organizations can extract useful info that aids in optimizing community efficiency and enhancing safety measures.

NetFlow, developed by Cisco, aggregates site visitors into flows based mostly on key fields comparable to IP addresses and port numbers, permitting for the identification of distinctive classes between gadgets. This supplies useful details about community customers, purposes, and site visitors routing.

IPFIX, an IETF normal, extends NetFlow’s capabilities by providing a extra versatile framework for exporting move info, supporting a variety of knowledge varieties and customization for particular monitoring wants. Each protocols are instrumental in community administration, serving to organizations detect anomalies, optimize efficiency, and guarantee safety by understanding communication patterns and information alternate volumes.

Assets

Cisco Telemetry Structure Information
Cisco Safe Community Analytics + Splunk
Cisco Nexus 9000 Sequence NX-OS Programmability Information, Launch 10.2(x)

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Google’s AI Overviews and the Destiny of the Open Internet

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Google’s AI Overviews and the Destiny of the Open Internet


Google’s search outcomes are present process an enormous change. As a substitute of the acquainted record of blue hyperlinks, many customers now see AI-generated abstract solutions – what Google calls “AI Overviews” – on the prime of their search pages. Early information exhibits this shift is already draining clicks from web sites: after AI Overviews rolled out, clicks to top-ranked websites dropped by over 34%. For the common web person, it means getting prompt solutions with out leaving Google. However behind that comfort lies rising unease about what this implies for public entry to info, the livelihoods of content material creators, the way forward for search engine optimization, and the stability of energy over the net’s information.

On this evaluation, I discover the broader implications of Google’s AI Overviews. How does an AI-curated reply change the best way folks discover info? Can journalists, publishers, and different creators maintain their work when fewer readers click on by? What turns into of search engine optimization and content material discoverability in a world of “zero-click” searches? And in the end, is it wholesome for a handful of AI programs to gatekeep what info all of us see? These questions lower to the core of whether or not Google’s AI is suitable with a vibrant, open net – or quietly undermining it.

From Ten Blue Hyperlinks to One Reply

For twenty years, Google Search served as a gateway to a plurality of sources – the blue hyperlinks providing a choice of web sites for any question. AI Overviews change that paradigm by condensing info from a number of sources right into a single synthesized reply on the prime of the web page. Google touts this as a boon for customers, claiming the AI abstract helps folks ask extra advanced questions and shortly discover high-quality content material. Certainly, not having to click on by a number of websites to assemble a solution is handy. Advanced questions that after required scouring a number of pages can now be answered in a single go, in plain language, proper on Google’s display screen.

Nevertheless, this one-stop reply comes at the price of breadth and context. When an AI Overview supplies what appears like a definitive response, customers could not really feel the necessity to look at different sources or viewpoints. They won’t even scroll all the way down to see the normal outcomes anymore. This raises the stakes for accuracy – and there have been early missteps. At first, Google’s AI Overview function served up doubtful and even harmful misinformation in its responses, from suggesting folks “add some glue” to pizza dough to advising they eat a small rock day by day. Many customers will settle for an authoritative-sounding reply at face worth, particularly when it’s offered by Google. The general public could also be much less conscious of errors and fewer uncovered to the nuance that a number of sources present.

There’s additionally a priority in regards to the variety of knowledge that customers see. Google’s AI summaries do embody citations, however they overwhelmingly draw on a slim set of “trusted” web sites. One evaluation discovered that over 93% of hyperlinks cited in AI Overviews come from the highest 10 conventional search outcomes. In principle this concentrate on authoritative sources helps scale back inaccuracies (Google has famous it solely exhibits info backed by high-ranking outcomes. Nevertheless it additionally means the AI is essentially amplifying the same old dominant voices. Area of interest blogs, rising views, or minority viewpoints which may have appeared decrease on web page one are much less prone to floor in an AI-curated reply. Public entry to info may change into extra homogenized – extremely filtered by Google’s AI standards – slightly than the open exploration the net as soon as promised.

CTR decline following introduction of AI Overviews (Ahrefs)

Content material Creation in a No-Click on World

The common click-through price (CTR) for Google’s prime natural search end result plummeted from about 7.3% in March 2024 to 2.6% in March 2025 after AI Overviews rolled out. Fewer clicks imply fewer readers touchdown on the websites that really produce the data – a worrisome pattern for anybody who earns a residing by publishing content material on-line. Prior to now, capturing the #1 spot on Google may flood a web site with visitors. Now, even being ranked first could yield solely a trickle of tourists if the AI snippet already solutions the question. This decline immediately undermines Google’s earlier assurances that AI solutions would improve engagement with net content material; no less than one Google govt had argued that the brand new AI outcomes may enhance click-through charges. That declare, as skeptics anticipated, has not borne out – if something, the other is going on.

A number of research now verify what many publishers feared: person clicks are dropping considerably when AI summaries seem. The latest evaluation by Ahrefs (referenced above) discovered a 34.5% drop in click-through for the highest natural end result after AI Overviews launched. Ahrefs’ information – drawn from lots of of 1000’s of searches – suggests this isn’t a minor blip however a broad sample. Its content material advertising and marketing director predicts click on charges will solely proceed to slip as customers develop extra accustomed to getting solutions straight from Google. And the highest end result isn’t the one casualty; when fewer folks click on the primary hyperlink, the cascade impact means even fewer trickle all the way down to the second, third, and past.

Google’s personal interface usually pushes the whole record of conventional outcomes far down the web page, generally requiring customers to scroll previous a full web page of AI reply and associated content material earlier than natural hyperlinks seem. It’s no marvel that roughly 60% of searches now finish with none click-through in any respect.

For publishers, journalists, and impartial creators, this pattern is sounding alarms. Their content material is being scraped, summarized, and served up by Google, however fewer customers are clicking by to provide them the web page views, advert impressions, or income alternatives wanted to fund that content material. If a web site’s visitors drops by a 3rd, its advert income and viewers development are inclined to drop in tandem. Some fear a couple of vicious cycle: as clicks and revenues fall, websites must in the reduction of on content material or put up paywalls, which in flip diminishes the freely accessible info that Google’s AI can draw upon. In excessive situations, AI-driven search may nearly get rid of visitors to many web sites – one examine discovered that when customers get solutions from a pure AI search engine (like OpenAI’s or Perplexity’s instruments), referrals to information websites and blogs collapsed by 96% in comparison with traditional Google search. Even a fraction of that affect in Google’s ecosystem can be devastating. To outlive, content material producers more and more understand they may have to search out new methods to align with the AI period of search.

Google AI Overview (Unite AI/Alex McFarland)

search engine optimization within the Age of Generative Search

These modifications additionally upend the normal playbook of SEO (search engine optimization). For years, search engine optimization specialists optimized content material to rank excessive on Google’s end result web page, believing {that a} prime rating ensures consideration. Now, even high-ranked pages can change into invisible in the event that they’re buried below an expansive AI reply field. Entrepreneurs are observing a dramatic rise in “zero-click” searches, with Google answering queries outright on the outcomes web page.

Moreover, Google’s AI Overviews have been recognized to displace top-ranked hyperlinks by as a lot as 1,500 pixels – about two full screens on desktop (and much more on cell). In observe, this implies a person may need to scroll loads earlier than they even see the primary natural hyperlink. The struggle for visibility has by no means been fiercer, and the previous search engine optimization system has been thrown out the window.

Unsurprisingly, companies and publishers are racing to adapt. The purpose now is not only to rank, however to be featured contained in the AI reply. We’re seeing the rise of what some name “Reply Engine Optimization” – tweaking content material in order that Google’s AI is extra prone to choose it up in its abstract. Google’s system closely favors content material it deems authoritative (practically all sources cited by AI Overviews come from the primary web page of outcomes, so the normal pillars of search engine optimization like high quality content material and backlinks stay vital. However past that, content material creators are experimenting with new techniques: writing in a Q&A format, including structured information, and explicitly answering possible person queries inside their textual content, hoping the AI will incorporate their materials. In essence, web site homeowners are studying to attraction to Google’s AI itself. The companies that optimize now for AI-driven outcomes will safe their place, whereas people who don’t danger changing into invisible.

Curiously, when you do handle to be included as a supply in an AI overview, the standard of visitors you obtain would possibly enhance whilst amount falls. Early proof means that guests who click on by from an AI-curated reply are usually extremely engaged. This is smart – if the AI overview funnels a person to a particular article, it’s probably as a result of that article squarely addresses what the person is on the lookout for. In response to criticism that AI solutions hold folks on Google, the corporate has additionally began including extra direct hyperlinks into the AI summaries and even integrating adverts alongside them. 

These modifications are supposed to drive customers to writer web sites and reassure content material creators that Google’s AI isn’t a dead-end for his or her visitors. Nonetheless, for many publishers, the stability of energy has clearly shifted: it’s Google’s AI that orchestrates discoverability, and everyone seems to be scrambling to work inside (or round) that new actuality.

AI Gatekeepers and the Internet’s Future

The rise of AI-generated solutions raises a profound query: who controls what info the general public sees? Within the period of blue hyperlinks, Google’s affect was huge however it nonetheless acted largely as an index and referee – presenting a variety of sources and letting customers select. With AI Overviews, Google (and its algorithms) tackle a stronger editorial function, synthesizing and filtering information earlier than the person even clicks something. If this mannequin turns into the norm, the corporate’s AI successfully turns into the gatekeeper of the open net, and different AI search instruments may play an identical function. 

Google insists that “serving to folks uncover content material from the net stays central to our method,” however the actuality is that AI mediation provides it much more management over what will get proven, cited, or ignored. The foremost tech and AI suppliers are poised to change into the first gatekeepers of knowledge entry, setting the phrases for which information or opinions a searcher sees. For publishers and creators, reaching an viewers could more and more require appeasing these gatekeepers – conforming to the codecs or pointers that the AI favors.

This consolidation of informational energy has moral and financial ramifications. On the moral aspect, there’s the problem of bias and variety: if a handful of AI programs decide the “greatest” reply for tens of millions of customers, there’s a danger that minority views or rising info get systematically filtered out. Media analysts warn that this pattern may focus affect over international info and shrink the variety of voices, primarily inserting a lot of the world’s information curation within the fingers of some U.S. tech corporations. 

On the financial aspect, AI-generated solutions are constructed on content material from the open net – content material somebody paid to create – but those self same solutions could deprive content material creators of the visitors and income they should survive. It’s a paradox of worth extraction: AI wants a thriving open net to feed it information, however by siphoning consideration away from web sites, it undermines the net’s very basis. To additional complicate issues, some AI corporations have began placing offers with giant publishers for direct entry to their content material. These licensing preparations (usually behind closed doorways) may create a two-tier system the place massive, established retailers get compensated and prioritized in AI outcomes, whereas smaller impartial websites are not noted within the chilly. 

In the meantime, net scraping by AI fashions has skyrocketed, indicating that AIs are gobbling up extra on-line content material than ever, whilst they ship proportionally much less visitors again. In different phrases, the AI is taking extra and giving much less.

What does all this imply for the way forward for the open net? There’s an actual chance that, if left unchecked, we head towards an internet the place high quality info exists however is generally consumed secondhand by AI intermediaries slightly than immediately on web sites. The motivation to create may dwindle for a lot of creators if their work not often will get direct readership or reward. Over time, a shrinking pool of content material would possibly even degrade the standard of AI solutions, since these fashions depend on recent, various info to remain correct. Avoiding that adverse spiral will probably require acutely aware effort and new norms or insurance policies. 

Some counsel that Google and different AI search suppliers could have to share a portion of advert revenues with content material publishers, or tweak their AI UIs to extra prominently encourage exploration of sources. Regulators, too, are paying consideration – drawing parallels to previous battles over how information aggregators use writer content material. On the finish of the day, preserving an open, vibrant net ecosystem within the age of AI might be a collective problem. 

Google’s AI Overviews characterize a formidable leap in search comfort and functionality, however making certain that this innovation advantages customers and content material creators alike is the important thing. The online was constructed on an open change of knowledge, and it’s in our greatest curiosity – as customers, as creators, as a society – to ensure that change doesn’t change into a one-way avenue managed by AI. The approaching years will decide whether or not Google’s AI-enhanced search can actually complement the open net, or whether or not it can slowly enclose it.

Google AI Overviews (FAQs)

1. How do AI Overviews differ from conventional search outcomes?

They substitute blue-link lists with AI-generated summaries, decreasing the necessity to click on by to web sites.

2. What are the principle advantages of utilizing AI Overviews?

They provide quick, synthesized solutions to advanced questions immediately within the search outcomes.

3. How does AI Overviews affect search engine optimization methods?

They shift focus from rating excessive to being cited in AI solutions, making “Reply Engine Optimization” important.

4. What sorts of questions are greatest suited to AI Overviews?

Multi-part, informational, or research-style questions that require synthesis throughout sources.

5. How does AI Overviews deal with advanced queries?

By drawing from a number of top-ranking sources to generate simplified, conversational responses.

Police Division Saves Hundreds Of {Dollars} Every Month Utilizing Electrical Automobiles

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Police Division Saves Hundreds Of {Dollars} Every Month Utilizing Electrical Automobiles


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A latest information story highlighted how the Bargersville, Indiana Police Division is saving some huge cash through the use of electrical autos as a substitute of gas-powered ones. Electrical motors are extra vitality environment friendly than inside combustion engines, and never by a little bit. Electrical energy additionally prices lower than gasoline, and EVs will be charged at evening when the charges are lowest.

Electrical autos could make glorious fleet autos as a result of they might function in an area space primarily or completely the place they will make their runs and return to residence base for normal charging. Though it’s not that widespread for the time being, some fleet homeowners could receive their very own rooftop or cover solar energy programs and cost their electrical autos with self-generated electrical energy.

At CleanTechnica, we’ve got beforehand reported on police departments utilizing electrical autos efficiently. So as to add yet another, right here is an interview with Bargersville PD’s Chief of Police, Todd Bertram.

Picture courtesy of Chief Todd Bertram

What number of Teslas are in your fleet presently and which of them?

We’ve got 4 Tesla Mannequin 3’s and 10 Tesla Mannequin Y’s.

Do you could have onsite chargers for all of them?

We’ve got eight Degree 2 charging stations and one DC quick charger.

Is your fleet all electrics or are there some gasoline autos too?

We’ve got a 2023 Ford F-150 Lightning and three different gasoline Ford F-150s.

About how a lot does the police division save every month through the use of electrical autos?

Nicely, it is a laborious query: for 3 vehicles in March of 2025 we spent $1,400 and for 14 electrical vehicles we spent $765. So, if we assume we had all gasoline vehicles we’d have spent $7,650, with a financial savings of $6,885 each month.

Is the financial savings solely from gas financial savings or is there some from less expensive upkeep and repairs too?

The above numbers are simply from gas. We additionally don’t do oil adjustments on 14 of the vehicles — that may be a further financial savings of $1,190 about each 2 months.

Does the police division have some rooftop photo voltaic to cost the EVs or if not, will you get some solar energy?

No, we don’t however that may certain save much more cash sooner or later perhaps.

Are there every other advantages to utilizing the Teslas such because the quietness and quickness in comparison with gasoline autos?

Quicker turn-around instances when issues do must be carried out and fewer down time.

Would you ever return to an all-gas car fleet?

No approach, we would want so as to add one other $100,000 to the tax payers burden and I don’t assume the council would need to try this.

Whether or not you could have solar energy or not, please full our newest solar energy survey.

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ios – Cannot confirm Apple receipt because of the measurement

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ios – Cannot confirm Apple receipt because of the measurement


I am attempting to setup fee processing for my iOS app. As soon as the fee receipt is generated in iOS – we ship it to Node.JS backend buy_subscription endpoint, which verifies receipt utilizing VerifyAppleReceipt perform, generates order knowledge to be inserted into DB utilizing generateOrderData perform. Nevertheless, when I attempt to buy my auto-renewable subscription – I consistently get backend error:

PayloadTooLargeError: request entity too massive
    at readStream 
    at getRawBody 
    at learn 
    at jsonParser 
    at Layer.deal with [as handle_request] 
    at trim_prefix 
    at 
    at Operate.process_params 
    at subsequent 
    at expressInit

In some unspecified time in the future previously I used to be in a position to course of the acquisition appropriately and write generated knowledge into DB.
I think the scale of receipt has one thing to do with it. Since I take advantage of Apple sandbox account for testing – each sandbox buy generates 6 purchases in latest_receipt_info of Apple Receipt (5 minutes * 6 renewals = 1 month of sandox). So presently my poor receipt has 254 operations in it.

const verifyAppleReceipt = async (receiptData) => {
        addLog(`[verifyAppleReceipt] Beginning verification`);
        addLog(`[verifyAppleReceipt] Receipt preview: ${receiptData.substring(0, 20)}...`);
        
        const requestPayload = {
            'receipt-data': receiptData,
            'password': 'exampleData',
            'exclude-old-transactions': true
        };
        addLog(`[verifyAppleReceipt] Payload keys: ${Object.keys(requestPayload).be a part of(',')}`);

        strive {
            addLog(`[verifyAppleReceipt] Requesting manufacturing endpoint...`);
            let response = await axios.submit('https://purchase.itunes.apple.com/verifyReceipt', requestPayload);
            addLog(`[verifyAppleReceipt] Manufacturing response standing: ${response.knowledge.standing}`);
            
            if (response.knowledge.standing === 21007) {
                addLog(`[verifyAppleReceipt] 21007 = Sandbox receipt, switching to sandbox endpoint...`);
                response = await axios.submit('https://sandbox.itunes.apple.com/verifyReceipt', requestPayload);
                addLog(`[verifyAppleReceipt] Sandbox response standing: ${response.knowledge.standing}`);
            }
            return response.knowledge;
        } catch (error) {
            addLog(`[verifyAppleReceipt] Error: ${error.message}`);
            throw error;
        }
    };

    async perform generateOrderData({ parsedReceipt, uid, receiptData, productId, quantity, isAutoRenewable }) {
        strive {
            const transaction = parsedReceipt.latest_receipt_info?.slice(-1)[0] || parsedReceipt.receipt?.in_app?.slice(-1)[0];
            if (!transaction) throw new Error('No legitimate transaction present in receipt');

            const order_id = `APPLE${transaction.transaction_id}`;
            const transactionDate = new Date(parseInt(transaction.purchase_date_ms));

            console.log("[GENERATE ORDER] Getting member information for:", uid);
            const memResult = await executeQuery(`SELECT idx FROM MemberInfo WHERE id = N'${uid}'`);

            if (!Array.isArray(memResult) || memResult.size === 0) throw new Error('Person not discovered');
            const mem_idx = memResult[0].idx;

            const item_settle_choice = isAutoRenewable ? 1 : (quantity === 1 ? 1 : quantity === 6 ? 2 : quantity === 12 ? 3 : 0);

            console.log("[GENERATE ORDER] Getting present product information for:", mem_idx);
            const existingProResult = await executeQuery(`SELECT pro_date_start FROM tv_myitem_tb WHERE mem_idx = ${mem_idx}`);

            const existingProStart = existingProResult?.[0]?.pro_date_start;
            const pro_date_start = existingProStart || transactionDate;

            const daysToAdd = isAutoRenewable ? 3 : (typeof quantity === 'quantity' ? 30 * quantity : 0);
let pro_date_end = DateTime.fromJSDate(transactionDate).plus({ days: daysToAdd }).toJSDate();

            const worth = isAutoRenewable ? 13000 : (quantity === 1 ? 13000 : quantity === 6 ? 70000 : quantity === 12 ? 120000 : 0);

            return {
                order_id,
                regdate: transactionDate,
                item_settle_choice,
                mem_idx,
                mem_id: uid,
                pro_date_start,
                pro_date_end,
                receiptData,
                productId,
                worth
            };
        } catch (error) {
            console.error("[GENERATE ORDER] Error:", error);
            throw error;
        }
    }

    perform toUTCFromLocal(date) {
        return DateTime.fromJSDate(date).toUTC();  // Converts a JavaScript Date object to a Luxon DateTime object and returns UTC
    }

    router.submit("/buy_subscription", async perform (req, res) {
        strive {
            const { uid, receiptData, productId } = req.physique;
            console.log("[BUY MEMBERSHIP] Beginning buy for:", uid);

            const existingOrder = await executeQuery(`SELECT order_idxx FROM web_pay_apple_log WHERE productId = N'${productId}' AND receiptData = N'${receiptData}'`);

            if (Array.isArray(existingOrder) && existingOrder.size > 0) {
                return res.finish('{"rtnCd":"fail","rtnMsg":["Duplicate receipt detected"]}');
            }

            const appleResponse = await verifyAppleReceipt(receiptData);
            if (appleResponse.standing !== 0) throw new Error("Verification failed");

            const orderData = await generateOrderData({ parsedReceipt: appleResponse, uid, receiptData, productId, isAutoRenewable: true });

            const checkQuery = `SELECT pro_date_count FROM tv_myitem_tb WHERE pro_code="915" AND mem_idx = ${orderData.mem_idx}`;
            console.log("[BUY MEMBERSHIP] Checking present membership:", checkQuery);
            const checkResult = await executeQuery(checkQuery);

            const proDateCount = checkResult?.[0]?.pro_date_count || 0;

            if (proDateCount === 0) {
                const insertQuery = `
                    INSERT INTO tv_myitem_tb(mem_idx, mem_id, pro_cate, pro_code, pro_name, pro_gubun, pro_date_count, dnis, pro_date_start, pro_date_end)
                    SELECT idx, id, 1, '915', N'정회원 자동결제', 2, 1, '15', GETDATE(), DATEADD(dd, 3, GETDATE()) 
                    FROM MemberInfo WHERE idx = ${orderData.mem_idx}
                `;
                console.log("[BUY MEMBERSHIP] Inserting new membership:", insertQuery);
                await executeQuery(insertQuery);
            } else {
                const updateQuery = `
                    UPDATE tv_myitem_tb 
                    SET pro_date_end = DATEADD(dd, 3, GETDATE()) 
                    WHERE mem_idx = ${orderData.mem_idx} AND pro_code="915"
                `;
                console.log("[BUY MEMBERSHIP] Updating present membership:", updateQuery);
                await executeQuery(updateQuery);
            }

            const logQuery = `
                INSERT INTO web_pay_apple_log(order_idxx, id, cd_value, item_settle_choice, item_code_idx, item_name, cd_info, website, productId, receiptData, mem_idx)
                VALUES (N'${orderData.order_id}', N'${orderData.mem_id}', ${orderData.worth}, ${orderData.item_settle_choice}, '915', N'정회원 자동결제', '1', 'land', N'${orderData.productId}', N'${orderData.receiptData}', ${orderData.mem_idx})
            `;
            console.log("[BUY MEMBERSHIP] Logging transaction:", logQuery);
            await executeQuery(logQuery);

            res.finish('{"rtnCd":"success","rtnMsg":[""]}');
        } catch (error) {
            console.error("[BUY MEMBERSHIP] Error:", error);
            res.finish('{"rtnCd":"fail","rtnMsg":["Error processing payment"]}');
        }
    });

Since I do not see any debug logging – I think that no matter buy_membership does fails earlier than something is executed and I can not determine it out.

Any suggestions? I simply must confirm the receipt, parse it and get TransactionDate from it.

Your Information to Synthetic Intelligence From Scratch

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Your Information to Synthetic Intelligence From Scratch


Tech firms stay leaders within the adoption of generative synthetic intelligence (AI), as they used it in 88% of their capabilities in 2024, in response to Statista. AI applied sciences have built-in quickly into enterprise operations, primarily affecting advertising and gross sales capabilities by way of their deployment as inventive help instruments.

A quick-changing enterprise surroundings allows AI brokers to reinforce organizational operations by way of improved course of effectivity and customer support whereas decreasing the necessity for added personnel.

This information explains find out how to construct an AI agent for enterprise wants, beginning with primary ideas and shifting on to execution and enhancement steps.

What Is an AI Agent?

By its programmed intelligence an AI agent capabilities as a digital employee able to performing single duties by itself whereas studying from given knowledge and adjusting to new circumstances. AI brokers differ from primary automation instruments since they consider enter by way of programming code to make decisions.

Your Information to Synthetic Intelligence From Scratch

Examples of AI brokers:

  • Gross sales assistants function lead qualifiers and oversee relationship upkeep operations
  • Assist brokers to handle queries 24/7
  • Content material creation and advertising optimization capabilities are dealt with by content material help instruments
  • Info processing knowledge analysts that reveal very important info

AI brokers hyperlink synthetic intelligence know-how to enterprise workflows, producing autonomous methods that carry out repetitive jobs whereas gaining data by way of machine studying.

Why Companies Are Turning to AI Brokers

Varied industries undertake AI brokers at an accelerated charge for a number of necessary causes, together with:

  • Automation of routine work: AI brokers carry out routine duties by processing emails whereas classifying questions and conducting primary conversations to launch human personnel for complicated obligations.
  • Accelerated decision-making: AI brokers improve knowledge processing velocity  as a result of they produce analytic insights and actionable suggestions.
  • Diminished prices and errors: Enterprise operations grow to be extra worthwhile by way of automated processes that cut back errors that sometimes happen inside repetitive work procedures.
  • At all times-on availability: AI brokers show 24/7 operational functionality as a result of they perform with out requiring relaxation intervals, thus sustaining fixed service accessibility.

Key Elements of a Fashionable AI Agent

These are a number of fundamental components it’s essential to construct a working AI agent. Each performs an necessary position, and whereas the fundamentals are comparable for many brokers, some particulars may change relying on what you’re constructing.

  1. Job & Goal Definition: That is the place the agent’s fundamental job is outlined. It outlines what the agent is meant to do and what sorts of issues it ought to resolve.
  2. Person Interface: That is how customers work together with the agent — whether or not by way of textual content or voice chat, e-mail, or an API. It’s the bridge between individuals and the AI.
  3. Studying & Information Consumption: The agent gathers and learns from all accessible info, together with conversations with customers and knowledge from inside enterprise methods.
  4. Choice-Making Engine: The agent makes use of guidelines and machine studying fashions to know inputs and determine find out how to reply or act.
  5. Integrations & Knowledge Entry: To take motion, the agent connects to different instruments and methods — like data bases, web sites, or APIs — so it might discover info and get issues performed.

Earlier than beginning growth, it turns into essential to know how varied elements will allow AI brokers to perform inside your corporation surroundings.

5 Steps to Construct an Efficient AI Agent for Your Enterprise

Creating an AI agent from starting to finish might appear to be an advanced course of till you divide it into smaller procedural steps.

AI Agent for Your Business

Step 1: Outline Your Enterprise Use Case

AI agent growth usually begins with figuring out one distinct enterprise problem. A complete system that makes an attempt to handle each request ends in software program that completes no single job.

Begin your evaluation by way of workflow mapping to establish the repetitive and time-consuming procedures. To create benchmarks for measuring progress it’s essential to decide how a lot time and sources go into performing these duties now.

The evaluation begins by evaluating customer support transcripts by way of theme detection strategies and analyzing buying delays in buyer journeys whereas consulting worker suggestions concerning administrative obstacles. Analysis collected by way of these processes will show areas the place AI brokers can produce most short-term worth.

Bear in mind, the return on funding calculation for alternative prioritization will depend on end result enhancement and time discount.

Step 2: Choose Your Improvement Method

The following step includes deciding on an acceptable growth design based mostly on technical prospects, time availability, and funding sources.

You must choose no-code or low-code choices to launch initiatives quickly (inside weeks as an alternative of months) when your growth workforce has AI experience, the platform options match your use case, and it’s essential to affirm the idea as a place to begin. These platforms present quick implementation paths, but their functionality may constrain your necessities when you will have a lot of particular growing wants.

When deciding on a growth companion for customized work, it is best to search for specialization in your sector and request proof of success with duties equal to your enterprise. Evaluation knowledge privateness protocols and safety measures earlier than shifting ahead whereas acquiring details about steady upkeep procedures.

In truth, customized growth gives higher options for integration with proprietary methods and distinctive necessities that can’t discover help in customary options along with strict compliance and knowledge safety wants and plans to construct interconnected brokers.

Step 3: Put together Your Knowledge

The effectiveness of your AI agent primarily will depend on the standard of the information, its amount, and its relevance to the duty at hand.

Knowledge assortment and preparation:

  • Create a list of information: Arrange a list of current knowledge present in inside sources, together with buyer help tickets, chat logs, data base articles, inside paperwork, product catalogs, specs, buyer e-mail communications, and coaching supplies. Assessing knowledge sources helps establish helpful info and desires for processing unavailable knowledge.
  • Clear and format knowledge: Automobileeful work is required in the course of the knowledge preparation course of. Ensure that to guard any info that may establish prospects or workers — so long as it doesn’t intervene with how the AI agent works. Knowledge from completely different sources ought to observe the identical format and use constant terminology, so methods can perceive one another extra simply. Repair any errors or inconsistencies that would confuse the AI throughout coaching. Additionally, convert messy or irregular knowledge into structured codecs which can be simpler for machines to work with.
  • Arrange for studying: Pinpointing knowledge association strategies for studying success requires making pairs out of search questions and ideal response options. Create a number of real looking examples that show how customers can phrase their wants in comparable methods. The testing course of ought to comprise edge examples for sturdy system efficiency in sensible real-world use.
  • Implement knowledge governance: DData governance helps you handle who can entry knowledge, how knowledge is used, and the way modifications are tracked. It ensures that knowledge processes are well-documented to satisfy compliance necessities and that there are clear procedures for protecting info updated. Good governance additionally protects delicate knowledge and makes positive your AI agent at all times works with correct, present info. To coach the agent successfully, you’ll want sufficient high-quality examples. Most duties require no less than 100 examples for every kind of request the agent will deal with. Extra complicated duties, like superior programming, might have over 1,000 examples to achieve a dependable stage of accuracy.

Step 4: Configure Agent Behaviors and Guardrails

To show your AI agent into a real consultant of your organization — not only a easy automated device — it’s essential to set clear boundaries and outline the way it ought to function.

Agent Behaviors and Guardrails

Conduct configuration:

  • Communication model: The inspiration for outlining an agent’s communication model is deciding on a tone that matches model identification; formal, pleasant, technical, or empathetic expressions. Standardize the way in which your group begins and ends calls to strengthen your organization identification. A written set of particular character traits along with authorized terminologies and acronym capabilities maintain brokers constant of their model illustration throughout all interactions.
  • Choice frameworks: The operation of your agent will depend on choice frameworks that present organizational construction. You must design decision-making buildings consisting of bushes that decide correct agent responses in response to varied person inputs for frequent eventualities. The system must observe exact directions for shifting person issues onto human brokers when noticing person annoyance or receiving inquiries previous its info scope. Set particular confidence requirements for repetitive duties so the agent can deal with them independently whereas critical selections will need to have excessive certainty ranges.
  • Workflow integration: The dialog path goes by way of initiation and continues to decision by utilizing workflow integration. Handoff transition procedures for human-agent interplay ought to present a transparent passage of contextual info between methods in the course of the session. Procedures ought to decide which info the agent wants to amass throughout discussions whereas additionally establishing safety protocols for very important operations.

Establishing efficient guardrails:

  • Prohibited actions: Your agent must keep away from performing prohibited actions. The agent wants full steering for topic issues that should keep off-limits, the dealing with of delicate knowledge requests, and conditions demanding human checking. Your agent wants these boundaries to be inside areas that can neither endanger your corporation nor misrepresent your operations.
  • Safety parameters: Earlier than sharing delicate info all brokers should full authentication procedures in response to safety parameters. Your system ought to have exact tips for dealing with private knowledge and you could decide correct durations of inactive classes and verification measures for info accessibility relying on completely different eventualities.
  • Compliance concerns: Organizational compliance should meet the actual tips established by completely different trade requirements. Necessary disclosures ought to be included for related functions and organizations ought to present correct record-keeping mechanisms and keep audit trails for regulatory choice factors made by brokers. Create a complete report of all guardrails to function a foundation throughout coaching, testing, and governance procedures. The documentation grows in worth as your agent capabilities broaden and regulatory necessities change.

Step 5: Launch, Check and Iterate

Because the final section, it’s essential to take a look at the AI agent for knowledge assortment that results in purposeful enchancment cycles.

  • Inside testing: Begin with workers as pleasant testers who will simulate various buyer sorts throughout completely different eventualities. Everlasting testing of bizarre case eventualities and eventualities particular to your group should happen earlier than the system turns into accessible to exterior customers. The performance of your escalation course of wants verification to ensure respectable person experiences.
  • Managed launch: The primary stage of managed deployment consists of selecting a particular buyer section for the product launch. The deployment system should embody a day restriction for the presence of human backup workers to keep up danger management. To begin, introduce the brokers solely in conditions the place they’ve proven peak-level confidence of their functionality to deal with inquiries. An in depth monitoring system will detect and resolve operational issues rapidly all through the analysis interval.
  • A/B testing: A/B testing provides knowledge by way of its evaluation of agent competence in comparison with human operator competence when performing equal duties. The optimum dialog format together with response methods ought to be established by way of assessments to find out the simplest mixture. Consider buyer satisfaction rankings alongside the time wanted to resolve instances to find out operational efficacy between completely different service approaches.

Key Efficiency Indicators:

Metric Class Instance Metrics
Effectivity Decision time, first-contact decision charge, automation charge
High quality Accuracy, escalation charge, error charge, compliance rating
Buyer Expertise Buyer satisfaction rating, Web Promoter Rating, repeat contact charge
Enterprise Impression Price per interplay, income influenced, conversion charge

Generic suggestions assortment strategies ought to embody post-session surveys along with detailed escalated dialog evaluation and intermittent buyer discussions. Such an built-in technique gives very important outcomes alongside understanding for higher growth processes.

Incorporate a efficiency monitoring system that tracks every day within the launch section however switches to weekly check-ups when secure operations begin. The corporate ought to detect recurring points reported by prospects to create efficient enchancment plans.

Set up a set system of difficult buyer interactions which is able to strengthen coaching. Brokers ought to obtain common updates that incorporate new examples and efficiency refinements.

Frequent Errors to Keep away from When Constructing AI Brokers

The implementation of AI brokers leads well-resourced organizations into foreseeable obstacles even when beginning a brand new venture. Information of frequent implementation points will help your venture growth whereas steering you past costly mistaken turns.

Building AI Agents

  • Making an attempt to construct an all-purpose agent: One specialised agent performs higher than a single agent making an attempt to handle a number of issues at a median stage.
  • Neglecting ethics and privateness: Moral considerations and privateness issues want consideration all through design time when constructing new capabilities inside your AI agent.
  • Inadequate monitoring: AI brokers want steady monitoring to examine their efficiency high quality and stop the event of dangerous conduct.
  • Skipping the human overview loop: The development of AI doesn’t exchange the significance of human oversight because the most achieved AI methods perform greatest with human involvement for enchancment and dealing with uncommon conditions.
  • Unrealistic expectations: The power of AI brokers to reach duties stops when confronted with complicated eventualities.

When to DIY and When to Name within the Specialists

You must carry out the AI integration course of inside your group in case your utility focuses on outlined duties and has formatted high-quality knowledge that’s available.

Furthermore, your workforce ought to have sensible data concerning AI platform operation and growth moreover, if the agent’s integration with legacy methods just isn’t mandatory.

Specialists ought to be consulted in your utility if it wants complicated pure language processing capabilities that surpass frequent program implementations. Their specialised data turns into substantial when your system requires reference to completely different enterprise methods or must deal with managed knowledge sorts with strict safety protocols.

Future-Proofing Your AI Technique

As you broaden your AI capabilities, take into consideration shifting away from single-agent approaches to an ecosystem of specialised AI brokers.

  • Construct an agent framework: Your group wants to determine an agent framework that standardizes the creation and deployment of various brokers.
  • Deal with interoperability: Design agent methods with interoperable interfaces that allow them to alternate info with each other and with present group methods.
  • Plan for steady studying:  Steady studying ought to be deliberate by way of person suggestions loops which allow brokers to develop their expertise based mostly on interactions and achieved outcomes.
  • Keep versatile: The design of your technique ought to embody provisions to adapt to new capabilities as a result of AI know-how is quickly altering.

The highest organizations now develop “agent networks” which mix completely different specialised AI brokers to carry out particular duties alongside each other. One agent handles knowledge processing and the opposite offers with buyer interactions whereas a 3rd produces reviews.

Actual-World Use Instances for AI Brokers

AI brokers have varied purposes, together with customer support, gross sales and advertising, inside operations, and content material creation and administration.

Customer support By AI brokers companies can resolve buyer inquiries about orders and merchandise with immediate service that additionally creates uniform customer support experiences
Gross sales and Advertising The primary two levels of gross sales operations along with customized outreach grow to be automated by way of AI brokers who flag promising leads for follow-up by people
Inside Operations AI service brokers simplify every day jobs that embody constructing schedules together with doc entry and report preparation
Content material Creation and Administration AI instruments quicken content material manufacturing procedures by creating new texts whereas retaining firm requirements and enhancing current firm supplies

Conclusion

Constructing an efficient AI agent stems from understanding your corporation targets and having structured knowledge alongside cautious utility methods. Any enterprise can activate AI capabilities that automate operations, advance buyer relationships, and generate progress.

The preliminary step includes graduation. Your group ought to start small AI implementations to realize expertise. Your enterprise will achieve survival benefit sooner or later in case you undertake AI options proper now as a result of the evolution of labor strategies by way of know-how continues.

As an AI experience supplier, SCAND delivers full companies for creating particular person AI brokers that meet your corporation necessities. Our workforce guides you thru an entire course of that features use case definition together with knowledge preparation and extends to growth phases as much as testing and steady enhancement.

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